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Search, Evaluate, and Read Evidence","\u002Fen\u002Facademic-writing\u002F02-reading-and-literature-matrix","en\u002Facademic-writing\u002F02-reading-and-literature-matrix",{"title":27,"path":28,"stem":29},"3. From Sources to Synthesis and Argument","\u002Fen\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs","en\u002Facademic-writing\u002F03-arguments-outlines-and-paragraphs",{"title":31,"path":32,"stem":33},"4. Literature Review and a Defensible Gap","\u002Fen\u002Facademic-writing\u002F04-writing-the-literature-review","en\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":35,"path":36,"stem":37},"5. Research Design, Evidence, and Core Sections","\u002Fen\u002Facademic-writing\u002F05-core-sections-and-evidence","en\u002Facademic-writing\u002F05-core-sections-and-evidence",{"title":39,"path":40,"stem":41},"6. Citation, Paraphrasing, Integrity, and AI","\u002Fen\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity","en\u002Facademic-writing\u002F06-citation-paraphrasing-and-integrity",{"title":43,"path":44,"stem":45},"7. Revision, Review, and Submission","\u002Fen\u002Facademic-writing\u002F07-revision-style-and-submission","en\u002Facademic-writing\u002F07-revision-style-and-submission",{"title":47,"path":48,"stem":49},"8. Research Workbook","\u002Fen\u002Facademic-writing\u002F08-literature-review-checklist-and-template","en\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":51,"path":52,"stem":53},"9. Research Workflow and Tools","\u002Fen\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management","en\u002Facademic-writing\u002F09-tools-for-academic-writing-and-literature-management",{"title":55,"path":56,"stem":57},"10. Worked Project — From Question to Defensible Conclusion","\u002Fen\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain","en\u002Facademic-writing\u002F10-worked-example-from-block-structure-to-question-chain",{"title":59,"path":60,"stem":61,"children":62,"page":249},"Accounting","\u002Fen\u002Faccounting","en\u002Faccounting",[63,69,87,93,193],{"title":64,"path":65,"stem":66,"children":67},"Accounting — From Evidence to Decisions","\u002Fen\u002Faccounting\u002F00-index","en\u002Faccounting\u002F00-index",[68],{"title":64,"path":65,"stem":66},{"title":70,"path":71,"stem":72,"children":73},"Accounting Appendix","\u002Fen\u002Faccounting\u002Fappendix","en\u002Faccounting\u002Fappendix\u002Findex",[74,75,79,83],{"title":70,"path":71,"stem":72},{"title":76,"path":77,"stem":78},"Worked Examples and Error Diagnosis","\u002Fen\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls","en\u002Faccounting\u002Fappendix\u002F24-examples-pitfalls",{"title":80,"path":81,"stem":82},"Accounting Glossary","\u002Fen\u002Faccounting\u002Fappendix\u002F26-glossary","en\u002Faccounting\u002Fappendix\u002F26-glossary",{"title":84,"path":85,"stem":86},"Reading and Evidence Map","\u002Fen\u002Faccounting\u002Fappendix\u002F27-reading-map","en\u002Faccounting\u002Fappendix\u002F27-reading-map",{"title":88,"path":89,"stem":90,"children":91},"Northstar Record-to-Decision Capstone","\u002Fen\u002Faccounting\u002Fcapstone","en\u002Faccounting\u002Fcapstone\u002Findex",[92],{"title":88,"path":89,"stem":90},{"title":94,"path":95,"stem":96,"children":97},"Financial Accounting","\u002Fen\u002Faccounting\u002Ffinancial-accounting","en\u002Faccounting\u002Ffinancial-accounting\u002Findex",[98,99,117,131,165,179],{"title":94,"path":95,"stem":96},{"title":100,"path":101,"stem":102,"children":103},"1. Foundations","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002Findex",[104,105,109,113],{"title":100,"path":101,"stem":102},{"title":106,"path":107,"stem":108},"Objectives and Qualitative Characteristics","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F01-objectives-qualitative-characteristics",{"title":110,"path":111,"stem":112},"Equation and Elements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F02-equation-elements",{"title":114,"path":115,"stem":116},"Accrual Basis, Estimates and Periods","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles","en\u002Faccounting\u002Ffinancial-accounting\u002F01-foundations\u002F03-accounting-bases-principles",{"title":118,"path":119,"stem":120,"children":121},"2. Recording Transactions","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002Findex",[122,123,127],{"title":118,"path":119,"stem":120},{"title":124,"path":125,"stem":126},"Double-Entry and Debit\u002FCredit","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F04-double-entry-dr-cr",{"title":128,"path":129,"stem":130},"Accounting Cycle","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle","en\u002Faccounting\u002Ffinancial-accounting\u002F02-recording-transactions\u002F05-accounting-cycle",{"title":132,"path":133,"stem":134,"children":135},"3. Measurement and Adjustments","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002Findex",[136,137,141,145,149,153,157,161],{"title":132,"path":133,"stem":134},{"title":138,"path":139,"stem":140},"Revenue Recognition","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F07-revenue-recognition",{"title":142,"path":143,"stem":144},"Inventory","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F08-inventory",{"title":146,"path":147,"stem":148},"Receivables and Expected Credit Losses","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F09-receivables",{"title":150,"path":151,"stem":152},"Property, Plant and Equipment","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F10-ppe",{"title":154,"path":155,"stem":156},"Intangible Assets","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F11-intangibles",{"title":158,"path":159,"stem":160},"Leases","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F12-leases",{"title":162,"path":163,"stem":164},"Current and Deferred Income Tax","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax","en\u002Faccounting\u002Ffinancial-accounting\u002F03-measurement-adjustments\u002F13-income-tax",{"title":166,"path":167,"stem":168,"children":169},"4. Reporting and Cash","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002Findex",[170,171,175],{"title":166,"path":167,"stem":168},{"title":172,"path":173,"stem":174},"Linked Financial Statements","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F06-financial-statements",{"title":176,"path":177,"stem":178},"Cash, Reconciliation and Internal Control","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control","en\u002Faccounting\u002Ffinancial-accounting\u002F04-reporting-cash\u002F14-cash-control",{"title":180,"path":181,"stem":182,"children":183},"5. Analysis and Comparison","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002Findex",[184,185,189],{"title":180,"path":181,"stem":182},{"title":186,"path":187,"stem":188},"Ratio Analysis","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F15-ratio-analysis",{"title":190,"path":191,"stem":192},"IFRS versus US GAAP","\u002Fen\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap","en\u002Faccounting\u002Ffinancial-accounting\u002F05-analysis-comparison\u002F25-ifrs-gaap",{"title":194,"path":195,"stem":196,"children":197},"Management Accounting","\u002Fen\u002Faccounting\u002Fmanagement-accounting","en\u002Faccounting\u002Fmanagement-accounting\u002Findex",[198,199,217,235],{"title":194,"path":195,"stem":196},{"title":200,"path":201,"stem":202,"children":203},"1. Cost Foundations","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002Findex",[204,205,209,213],{"title":200,"path":201,"stem":202},{"title":206,"path":207,"stem":208},"Cost Concepts and Behaviour","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F16-cost-concepts",{"title":210,"path":211,"stem":212},"Costing Systems","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F17-costing-systems",{"title":214,"path":215,"stem":216},"Variable and Absorption Costing","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption","en\u002Faccounting\u002Fmanagement-accounting\u002F01-cost-foundations\u002F19-variable-vs-absorption",{"title":218,"path":219,"stem":220,"children":221},"2. Planning and Control","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002Findex",[222,223,227,231],{"title":218,"path":219,"stem":220},{"title":224,"path":225,"stem":226},"Cost–Volume–Profit Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F18-cvp-analysis",{"title":228,"path":229,"stem":230},"Budgeting and Variance Analysis","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F20-budgeting-variances",{"title":232,"path":233,"stem":234},"Performance Measurement","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement","en\u002Faccounting\u002Fmanagement-accounting\u002F02-planning-control\u002F21-performance-measurement",{"title":236,"path":237,"stem":238,"children":239},"3. Decisions and Investment","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002Findex",[240,241,245],{"title":236,"path":237,"stem":238},{"title":242,"path":243,"stem":244},"Short-Term Decisions","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F22-short-term-decisions",{"title":246,"path":247,"stem":248},"Capital Budgeting","\u002Fen\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting","en\u002Faccounting\u002Fmanagement-accounting\u002F03-decisions-investment\u002F23-capital-budgeting",false,{"title":251,"path":252,"stem":253,"children":254,"page":249},"Business Analytics","\u002Fen\u002Fbusiness-analytics","en\u002Fbusiness-analytics",[255,261,279,305,335,365,383,400],{"title":256,"path":257,"stem":258,"children":259},"Business Analytics — From Data to Defensible Action","\u002Fen\u002Fbusiness-analytics\u002F00-index","en\u002Fbusiness-analytics\u002F00-index",[260],{"title":256,"path":257,"stem":258},{"title":262,"path":263,"stem":264,"children":265},"0. Decision and Data Foundations","\u002Fen\u002Fbusiness-analytics\u002F00-intro","en\u002Fbusiness-analytics\u002F00-intro\u002Findex",[266,267,271,275],{"title":262,"path":263,"stem":264},{"title":268,"path":269,"stem":270},"Decision Framing","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing","en\u002Fbusiness-analytics\u002F00-intro\u002F01-decision-framing",{"title":272,"path":273,"stem":274},"Data Contracts","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts","en\u002Fbusiness-analytics\u002F00-intro\u002F02-data-contracts",{"title":276,"path":277,"stem":278},"Reproducible Analytics Workflow","\u002Fen\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow","en\u002Fbusiness-analytics\u002F00-intro\u002F03-reproducible-workflow",{"title":280,"path":281,"stem":282,"children":283},"1. Descriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive","en\u002Fbusiness-analytics\u002F01-descriptive\u002Findex",[284,285,289,293,297,301],{"title":280,"path":281,"stem":282},{"title":286,"path":287,"stem":288},"Distributions and Exploratory Data Analysis","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda","en\u002Fbusiness-analytics\u002F01-descriptive\u002F04-distributions-eda",{"title":290,"path":291,"stem":292},"KPIs and Denominators","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators","en\u002Fbusiness-analytics\u002F01-descriptive\u002F05-kpis-denominators",{"title":294,"path":295,"stem":296},"Segments, Cohorts and Funnels","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels","en\u002Fbusiness-analytics\u002F01-descriptive\u002F06-segmentation-cohorts-funnels",{"title":298,"path":299,"stem":300},"Visual Evidence and Data Stories","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story","en\u002Fbusiness-analytics\u002F01-descriptive\u002F07-visualisation-story",{"title":302,"path":303,"stem":304},"Experiments and Causal Boundaries","\u002Fen\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary","en\u002Fbusiness-analytics\u002F01-descriptive\u002F08-experiments-causal-boundary",{"title":306,"path":307,"stem":308,"children":309},"2. Predictive Analytics","\u002Fen\u002Fbusiness-analytics\u002F02-predictive","en\u002Fbusiness-analytics\u002F02-predictive\u002Findex",[310,311,315,319,323,327,331],{"title":306,"path":307,"stem":308},{"title":312,"path":313,"stem":314},"Validation, Baselines and Leakage","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage","en\u002Fbusiness-analytics\u002F02-predictive\u002F09-validation-leakage",{"title":316,"path":317,"stem":318},"Regression and Forecasting","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting","en\u002Fbusiness-analytics\u002F02-predictive\u002F10-regression-forecasting",{"title":320,"path":321,"stem":322},"Classification and Calibration","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration","en\u002Fbusiness-analytics\u002F02-predictive\u002F11-classification-calibration",{"title":324,"path":325,"stem":326},"Trees and Ensembles","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles","en\u002Fbusiness-analytics\u002F02-predictive\u002F12-trees-ensembles",{"title":328,"path":329,"stem":330},"Decision Metrics and Thresholds","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds","en\u002Fbusiness-analytics\u002F02-predictive\u002F13-decision-metrics-thresholds",{"title":332,"path":333,"stem":334},"Explainability, Monitoring and Drift","\u002Fen\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift","en\u002Fbusiness-analytics\u002F02-predictive\u002F14-explainability-drift",{"title":336,"path":337,"stem":338,"children":339},"3. Prescriptive Analytics","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive","en\u002Fbusiness-analytics\u002F03-prescriptive\u002Findex",[340,341,345,349,353,357,361],{"title":336,"path":337,"stem":338},{"title":342,"path":343,"stem":344},"Decision-Making under Uncertainty","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F15-decision-uncertainty",{"title":346,"path":347,"stem":348},"Linear Optimisation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F16-linear-optimization",{"title":350,"path":351,"stem":352},"Inventory and Allocation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F17-inventory-allocation",{"title":354,"path":355,"stem":356},"Queueing and Simulation","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F18-queueing-simulation",{"title":358,"path":359,"stem":360},"Pricing and Revenue Management","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F19-pricing-experimentation",{"title":362,"path":363,"stem":364},"Causal Targeting and Policy Learning","\u002Fen\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting","en\u002Fbusiness-analytics\u002F03-prescriptive\u002F20-causal-targeting",{"title":366,"path":367,"stem":368,"children":369},"4. Deployment and Governance","\u002Fen\u002Fbusiness-analytics\u002F04-deployment","en\u002Fbusiness-analytics\u002F04-deployment\u002Findex",[370,371,375,379],{"title":366,"path":367,"stem":368},{"title":372,"path":373,"stem":374},"Data Products and Monitoring","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring","en\u002Fbusiness-analytics\u002F04-deployment\u002F21-data-products-monitoring",{"title":376,"path":377,"stem":378},"Governance, Fairness and Privacy","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy","en\u002Fbusiness-analytics\u002F04-deployment\u002F22-governance-fairness-privacy",{"title":380,"path":381,"stem":382},"Adoption and Business Value","\u002Fen\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value","en\u002Fbusiness-analytics\u002F04-deployment\u002F23-adoption-value",{"title":384,"path":385,"stem":386,"children":387},"Appendix and Revision Tools","\u002Fen\u002Fbusiness-analytics\u002Fappendix","en\u002Fbusiness-analytics\u002Fappendix\u002Findex",[388,389,393,397],{"title":384,"path":385,"stem":386},{"title":390,"path":391,"stem":392},"Formula and Decision Map","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map","en\u002Fbusiness-analytics\u002Fappendix\u002F24-formula-map",{"title":394,"path":395,"stem":396},"Worked Examples and Pitfalls","\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls","en\u002Fbusiness-analytics\u002Fappendix\u002F25-worked-examples-pitfalls",{"title":84,"path":398,"stem":399},"\u002Fen\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map","en\u002Fbusiness-analytics\u002Fappendix\u002F26-reading-map",{"title":401,"path":402,"stem":403,"children":404},"Capstone — The Evening Delivery Promise","\u002Fen\u002Fbusiness-analytics\u002Fcapstone","en\u002Fbusiness-analytics\u002Fcapstone\u002Findex",[405],{"title":401,"path":402,"stem":403},{"title":407,"path":408,"stem":409,"children":410,"page":249},"Financial Economic Time Series","\u002Fen\u002Ffinancial-economic-time-series","en\u002Ffinancial-economic-time-series",[411,417,423,429,435,441,447,453,459,465,471],{"title":412,"path":413,"stem":414,"children":415},"Financial and Economic Time Series — Course Guide","\u002Fen\u002Ffinancial-economic-time-series\u002F00-intro","en\u002Ffinancial-economic-time-series\u002F00-intro\u002Findex",[416],{"title":412,"path":413,"stem":414},{"title":418,"path":419,"stem":420,"children":421},"Three Versions of Time Series","\u002Fen\u002Ffinancial-economic-time-series\u002F01-bridge","en\u002Ffinancial-economic-time-series\u002F01-bridge\u002Findex",[422],{"title":418,"path":419,"stem":420},{"title":424,"path":425,"stem":426,"children":427},"Data, Clocks, and Transformations","\u002Fen\u002Ffinancial-economic-time-series\u002F02-data-transformations","en\u002Ffinancial-economic-time-series\u002F02-data-transformations\u002Findex",[428],{"title":424,"path":425,"stem":426},{"title":430,"path":431,"stem":432,"children":433},"Predictive Regressions and Persistent Predictors","\u002Fen\u002Ffinancial-economic-time-series\u002F03-predictive-regressions","en\u002Ffinancial-economic-time-series\u002F03-predictive-regressions\u002Findex",[434],{"title":430,"path":431,"stem":432},{"title":436,"path":437,"stem":438,"children":439},"Volatility, Tails, and Financial Risk","\u002Fen\u002Ffinancial-economic-time-series\u002F04-volatility-risk","en\u002Ffinancial-economic-time-series\u002F04-volatility-risk\u002Findex",[440],{"title":436,"path":437,"stem":438},{"title":442,"path":443,"stem":444,"children":445},"Unit Roots, Cointegration, and Error Correction","\u002Fen\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration","en\u002Ffinancial-economic-time-series\u002F05-unit-roots-cointegration\u002Findex",[446],{"title":442,"path":443,"stem":444},{"title":448,"path":449,"stem":450,"children":451},"VAR, Structural Identification, and Local Projections","\u002Fen\u002Ffinancial-economic-time-series\u002F06-var-identification","en\u002Ffinancial-economic-time-series\u002F06-var-identification\u002Findex",[452],{"title":448,"path":449,"stem":450},{"title":454,"path":455,"stem":456,"children":457},"State Space, Mixed Frequency, and Nowcasting","\u002Fen\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting","en\u002Ffinancial-economic-time-series\u002F07-state-space-nowcasting\u002Findex",[458],{"title":454,"path":455,"stem":456},{"title":460,"path":461,"stem":462,"children":463},"Forecast Evaluation for Decisions","\u002Fen\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation","en\u002Ffinancial-economic-time-series\u002F08-forecast-evaluation\u002Findex",[464],{"title":460,"path":461,"stem":462},{"title":466,"path":467,"stem":468,"children":469},"Integrated R Laboratory","\u002Fen\u002Ffinancial-economic-time-series\u002F09-r-laboratory","en\u002Ffinancial-economic-time-series\u002F09-r-laboratory\u002Findex",[470],{"title":466,"path":467,"stem":468},{"title":472,"path":473,"stem":474,"children":475},"Capstones, Data, and Reading Ladder","\u002Fen\u002Ffinancial-economic-time-series\u002F10-capstone-readings","en\u002Ffinancial-economic-time-series\u002F10-capstone-readings\u002Findex",[476],{"title":472,"path":473,"stem":474},{"title":478,"path":479,"stem":480,"children":481,"page":249},"Intro To Economics","\u002Fen\u002Fintro-to-economics","en\u002Fintro-to-economics",[482,486,490,494,498,502,506,510,514,518,522,526,530],{"title":483,"path":484,"stem":485},"Introduction to Economics — Decisions, Markets, and the Macroeconomy","\u002Fen\u002Fintro-to-economics\u002F00-intro","en\u002Fintro-to-economics\u002F00-intro",{"title":487,"path":488,"stem":489},"Chapter 1 — Choice, Opportunity Cost, and Trade","\u002Fen\u002Fintro-to-economics\u002F01-foundations","en\u002Fintro-to-economics\u002F01-foundations",{"title":491,"path":492,"stem":493},"Chapter 2 — Demand, Supply, Equilibrium, and Welfare","\u002Fen\u002Fintro-to-economics\u002F02-demand-and-supply","en\u002Fintro-to-economics\u002F02-demand-and-supply",{"title":495,"path":496,"stem":497},"Chapter 3 — Elasticity, Revenue, and Tax Incidence","\u002Fen\u002Fintro-to-economics\u002F03-elasticity","en\u002Fintro-to-economics\u002F03-elasticity",{"title":499,"path":500,"stem":501},"Chapter 4 — Firms, Market Power, and Market Failure","\u002Fen\u002Fintro-to-economics\u002F04-market-structures","en\u002Fintro-to-economics\u002F04-market-structures",{"title":503,"path":504,"stem":505},"Chapter 5 — GDP, Income, Wealth, and Welfare","\u002Fen\u002Fintro-to-economics\u002F05-gdp-and-wealth","en\u002Fintro-to-economics\u002F05-gdp-and-wealth",{"title":507,"path":508,"stem":509},"Chapter 6 — Inflation, Purchasing Power, and Labour Markets","\u002Fen\u002Fintro-to-economics\u002F06-inflation-and-unemployment","en\u002Fintro-to-economics\u002F06-inflation-and-unemployment",{"title":511,"path":512,"stem":513},"Chapter 7 — Productivity, Technology, and Economic Growth","\u002Fen\u002Fintro-to-economics\u002F07-economic-growth","en\u002Fintro-to-economics\u002F07-economic-growth",{"title":515,"path":516,"stem":517},"Chapter 8 — Money, Credit, and Banking","\u002Fen\u002Fintro-to-economics\u002F08-money-and-banking","en\u002Fintro-to-economics\u002F08-money-and-banking",{"title":519,"path":520,"stem":521},"Chapter 9 — Business Cycles, AD–AS, and Monetary Policy","\u002Fen\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as","en\u002Fintro-to-economics\u002F09-monetary-policy-and-ad-as",{"title":523,"path":524,"stem":525},"Chapter 10 — Fiscal Policy, Distribution, and Public Debt","\u002Fen\u002Fintro-to-economics\u002F10-fiscal-policy","en\u002Fintro-to-economics\u002F10-fiscal-policy",{"title":527,"path":528,"stem":529},"Chapter 11 — Trade, Capital Flows, and Exchange Rates","\u002Fen\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates","en\u002Fintro-to-economics\u002F11-open-economy-and-exchange-rates",{"title":531,"path":532,"stem":533},"Chapter 12 — Integrated Economic Analysis Studio","\u002Fen\u002Fintro-to-economics\u002F12-review-and-case-studies","en\u002Fintro-to-economics\u002F12-review-and-case-studies",{"title":535,"path":536,"stem":537,"children":538,"page":249},"Microeconometrics","\u002Fen\u002Fmicroeconometrics","en\u002Fmicroeconometrics",[539,557,563,581,603,629,651,673,691,709],{"title":540,"path":541,"stem":542,"children":543},"0. Causal Questions and Designs","\u002Fen\u002Fmicroeconometrics\u002F00-foundations","en\u002Fmicroeconometrics\u002F00-foundations\u002Findex",[544,545,549,553],{"title":540,"path":541,"stem":542},{"title":546,"path":547,"stem":548},"Causal Questions and Estimands","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands","en\u002Fmicroeconometrics\u002F00-foundations\u002F01-causal-question-estimands",{"title":550,"path":551,"stem":552},"Potential Outcomes and Experiments","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments","en\u002Fmicroeconometrics\u002F00-foundations\u002F02-potential-outcomes-experiments",{"title":554,"path":555,"stem":556},"Causal Diagrams and Controls","\u002Fen\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls","en\u002Fmicroeconometrics\u002F00-foundations\u002F03-dags-controls",{"title":558,"path":559,"stem":560,"children":561},"Microeconometrics — Designing Credible Counterfactuals","\u002Fen\u002Fmicroeconometrics\u002F00-index","en\u002Fmicroeconometrics\u002F00-index",[562],{"title":558,"path":559,"stem":560},{"title":564,"path":565,"stem":566,"children":567},"1. Regression and Inference","\u002Fen\u002Fmicroeconometrics\u002F01-regression","en\u002Fmicroeconometrics\u002F01-regression\u002Findex",[568,569,573,577],{"title":564,"path":565,"stem":566},{"title":570,"path":571,"stem":572},"OLS as a Projection","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection","en\u002Fmicroeconometrics\u002F01-regression\u002F04-ols-projection",{"title":574,"path":575,"stem":576},"FWL, Selection and Controls","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls","en\u002Fmicroeconometrics\u002F01-regression\u002F05-fwl-selection-controls",{"title":578,"path":579,"stem":580},"Inference and Clustering","\u002Fen\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering","en\u002Fmicroeconometrics\u002F01-regression\u002F06-inference-clustering",{"title":582,"path":583,"stem":584,"children":585},"2. Instruments and Panel Data","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002Findex",[586,587,591,595,599],{"title":582,"path":583,"stem":584},{"title":588,"path":589,"stem":590},"Instrumental Variables","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F07-iv-identification",{"title":592,"path":593,"stem":594},"Weak Instruments and LATE","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F08-weak-iv-late",{"title":596,"path":597,"stem":598},"Panel Fixed Effects","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F09-panel-fixed-effects",{"title":600,"path":601,"stem":602},"Dynamic Panels and Limits","\u002Fen\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel","en\u002Fmicroeconometrics\u002F02-iv-panel\u002F10-dynamic-panel",{"title":604,"path":605,"stem":606,"children":607},"3. Policy Evaluation Designs","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs","en\u002Fmicroeconometrics\u002F03-policy-designs\u002Findex",[608,609,613,617,621,625],{"title":604,"path":605,"stem":606},{"title":610,"path":611,"stem":612},"Difference-in-Differences","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F11-did-core",{"title":614,"path":615,"stem":616},"Staggered DID and Event Studies","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F12-staggered-event-studies",{"title":618,"path":619,"stem":620},"Regression Discontinuity","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F13-rdd",{"title":622,"path":623,"stem":624},"Matching, Weighting and Overlap","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F14-matching-weighting",{"title":626,"path":627,"stem":628},"Synthetic Control","\u002Fen\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control","en\u002Fmicroeconometrics\u002F03-policy-designs\u002F15-synthetic-control",{"title":630,"path":631,"stem":632,"children":633},"Module 4 — Choice and Limited Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002Findex",[634,635,639,643,647],{"title":630,"path":631,"stem":632},{"title":636,"path":637,"stem":638},"Binary Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F16-binary-choice",{"title":640,"path":641,"stem":642},"Multinomial and Ordered Choice","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F17-multinomial-choice",{"title":644,"path":645,"stem":646},"Count Outcomes","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F18-count-outcomes",{"title":648,"path":649,"stem":650},"Censoring, Truncation and Selection","\u002Fen\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection","en\u002Fmicroeconometrics\u002F04-outcomes-choice\u002F19-censoring-selection",{"title":652,"path":653,"stem":654,"children":655},"Module 5 — Modern Causal Analysis","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal","en\u002Fmicroeconometrics\u002F05-modern-causal\u002Findex",[656,657,661,665,669],{"title":652,"path":653,"stem":654},{"title":658,"path":659,"stem":660},"Double Machine Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F20-dml",{"title":662,"path":663,"stem":664},"Heterogeneity and Policy Learning","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F21-heterogeneity-policy",{"title":666,"path":667,"stem":668},"Sensitivity and Partial Identification","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F22-sensitivity-partial-id",{"title":670,"path":671,"stem":672},"External Validity and Transport","\u002Fen\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity","en\u002Fmicroeconometrics\u002F05-modern-causal\u002F23-external-validity",{"title":674,"path":675,"stem":676,"children":677},"Module 6 — From Data to Auditable Evidence","\u002Fen\u002Fmicroeconometrics\u002F06-workflow","en\u002Fmicroeconometrics\u002F06-workflow\u002Findex",[678,679,683,687],{"title":674,"path":675,"stem":676},{"title":680,"path":681,"stem":682},"Data Provenance","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance","en\u002Fmicroeconometrics\u002F06-workflow\u002F24-data-provenance",{"title":684,"path":685,"stem":686},"Reproducibility and Reporting","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting","en\u002Fmicroeconometrics\u002F06-workflow\u002F25-reproducibility-reporting",{"title":688,"path":689,"stem":690},"Paper and Evidence Audit","\u002Fen\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit","en\u002Fmicroeconometrics\u002F06-workflow\u002F26-paper-audit",{"title":692,"path":693,"stem":694,"children":695},"Appendix — Revision and Evidence Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix","en\u002Fmicroeconometrics\u002Fappendix\u002Findex",[696,697,701,705],{"title":692,"path":693,"stem":694},{"title":698,"path":699,"stem":700},"Formula and Design Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map","en\u002Fmicroeconometrics\u002Fappendix\u002F27-formula-design-map",{"title":702,"path":703,"stem":704},"Ten Worked Microeconometric Pitfalls","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls","en\u002Fmicroeconometrics\u002Fappendix\u002F28-worked-pitfalls",{"title":706,"path":707,"stem":708},"Reading and Software Map","\u002Fen\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map","en\u002Fmicroeconometrics\u002Fappendix\u002F29-reading-software-map",{"title":710,"path":711,"stem":712,"children":713},"Capstone — Should Northbridge Expand Pathways?","\u002Fen\u002Fmicroeconometrics\u002Fcapstone","en\u002Fmicroeconometrics\u002Fcapstone\u002Findex",[714],{"title":710,"path":711,"stem":712},{"title":716,"path":717,"stem":718,"children":719,"page":249},"Microeconomics","\u002Fen\u002Fmicroeconomics","en\u002Fmicroeconomics",[720,726,732,738,744,750,756,762,768,774,780,786,792],{"title":721,"path":722,"stem":723,"children":724},"Advanced Microeconomics — Course Guide","\u002Fen\u002Fmicroeconomics\u002F00-intro","en\u002Fmicroeconomics\u002F00-intro\u002Findex",[725],{"title":721,"path":722,"stem":723},{"title":727,"path":728,"stem":729,"children":730},"Module 1 — Choice, Duality, and Revealed Preference","\u002Fen\u002Fmicroeconomics\u002F01-fundations","en\u002Fmicroeconomics\u002F01-fundations\u002Findex",[731],{"title":727,"path":728,"stem":729},{"title":733,"path":734,"stem":735,"children":736},"Module 2 — Comparative Statics and Welfare Measurement","\u002Fen\u002Fmicroeconomics\u002F02-comparative-statics","en\u002Fmicroeconomics\u002F02-comparative-statics\u002Findex",[737],{"title":733,"path":734,"stem":735},{"title":739,"path":740,"stem":741,"children":742},"Module 3 — Choice under Risk and Insurance","\u002Fen\u002Fmicroeconomics\u002F03-uncertainty","en\u002Fmicroeconomics\u002F03-uncertainty\u002Findex",[743],{"title":739,"path":740,"stem":741},{"title":745,"path":746,"stem":747,"children":748},"Module 4 — General Equilibrium and Welfare","\u002Fen\u002Fmicroeconomics\u002F04-general-equilibrium","en\u002Fmicroeconomics\u002F04-general-equilibrium\u002Findex",[749],{"title":745,"path":746,"stem":747},{"title":751,"path":752,"stem":753,"children":754},"Module 5 — Static, Dynamic, and Repeated Games","\u002Fen\u002Fmicroeconomics\u002F05-game-theory","en\u002Fmicroeconomics\u002F05-game-theory\u002Findex",[755],{"title":751,"path":752,"stem":753},{"title":757,"path":758,"stem":759,"children":760},"Module 6 — Oligopoly, Entry, and Algorithmic Pricing","\u002Fen\u002Fmicroeconomics\u002F06-oligopoly","en\u002Fmicroeconomics\u002F06-oligopoly\u002Findex",[761],{"title":757,"path":758,"stem":759},{"title":763,"path":764,"stem":765,"children":766},"Module 7 — Information Economics and Contracts","\u002Fen\u002Fmicroeconomics\u002F07-information-economics","en\u002Fmicroeconomics\u002F07-information-economics\u002Findex",[767],{"title":763,"path":764,"stem":765},{"title":769,"path":770,"stem":771,"children":772},"Module 8 — Mechanism Design and Auctions","\u002Fen\u002Fmicroeconomics\u002F08-mechanism-design","en\u002Fmicroeconomics\u002F08-mechanism-design\u002Findex",[773],{"title":769,"path":770,"stem":771},{"title":775,"path":776,"stem":777,"children":778},"Module 9 — Behavioural and Experimental Microeconomics","\u002Fen\u002Fmicroeconomics\u002F09-behavioural-economics","en\u002Fmicroeconomics\u002F09-behavioural-economics\u002Findex",[779],{"title":775,"path":776,"stem":777},{"title":781,"path":782,"stem":783,"children":784},"Module 10 — Externalities, Public Goods, and Collective Action","\u002Fen\u002Fmicroeconomics\u002F10-externalities-public-goods","en\u002Fmicroeconomics\u002F10-externalities-public-goods\u002Findex",[785],{"title":781,"path":782,"stem":783},{"title":787,"path":788,"stem":789,"children":790},"Module 11 — Matching and Market Design","\u002Fen\u002Fmicroeconomics\u002F11-market-design","en\u002Fmicroeconomics\u002F11-market-design\u002Findex",[791],{"title":787,"path":788,"stem":789},{"title":793,"path":794,"stem":795,"children":796},"Module 12 — Integrated Microeconomic Design Studio","\u002Fen\u002Fmicroeconomics\u002F12-review","en\u002Fmicroeconomics\u002F12-review\u002Findex",[797],{"title":793,"path":794,"stem":795},{"title":799,"path":800,"stem":801,"children":802},"Playground","\u002Fen\u002Fplayground","en\u002Fplayground\u002Findex",[803,804,808,812,816,820],{"title":799,"path":800,"stem":801},{"title":805,"path":806,"stem":807},"Typst Plugin Playground","\u002Fen\u002Fplayground\u002F01-typstex","en\u002Fplayground\u002F01-typstex",{"title":809,"path":810,"stem":811},"Citation Plugin Test","\u002Fen\u002Fplayground\u002F02-citation","en\u002Fplayground\u002F02-citation",{"title":813,"path":814,"stem":815},"Pyodide Playground","\u002Fen\u002Fplayground\u002F03-pyodide","en\u002Fplayground\u002F03-pyodide",{"title":817,"path":818,"stem":819},"WebR Playground","\u002Fen\u002Fplayground\u002F04-webr","en\u002Fplayground\u002F04-webr",{"title":821,"path":822,"stem":823},"Chart.js 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文献综述的构建与写作","\u002Fzh\u002Facademic-writing\u002F04-writing-the-literature-review","zh\u002Facademic-writing\u002F04-writing-the-literature-review",{"title":1056,"path":1057,"stem":1058},"8. 文献综述清单与模板","\u002Fzh\u002Facademic-writing\u002F08-literature-review-checklist-and-template","zh\u002Facademic-writing\u002F08-literature-review-checklist-and-template",{"title":1060,"path":1061,"stem":1062},"10. 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",[1800,1801,1802],"strong",{},"2026 年 8 月 1 日"," 的一手文献定向更新。所选论文覆盖正式期刊研究与方法综述；课堂模拟只展示统计机制，不含真实个人数据，也不复现原论文。",[1805,1806,1807],"h2",{"id":1807},"学习目标",[1793,1809,1810],{},"完成本章后，你应能够：",[1812,1813,1814,1818,1821,2050,2053,2056],"ol",{},[1815,1816,1817],"li",{},"区分大学录取、实际就读和毕业等不同处理；",[1815,1819,1820],{},"比较候补名单 IV 与录取分数 fuzzy RDD 所识别的不同边际人群；",[1815,1822,1823,1824,1923,1924,2049],{},"区分风险预测 ",[1825,1826,1829,1869],"span",{"className":1827},[1828],"katex",[1825,1830,1833],{"className":1831},[1832],"katex-mathml",[1834,1835,1837],"math",{"xmlns":1836},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[1838,1839,1840,1864],"semantics",{},[1841,1842,1843,1847,1852,1855,1858,1861],"mrow",{},[1844,1845,1846],"mi",{},"E",[1848,1849,1851],"mo",{"stretchy":1850},"false","[",[1844,1853,1854],{},"Y",[1848,1856,1857],{},"∣",[1844,1859,1860],{},"X",[1848,1862,1863],{"stretchy":1850},"]",[1865,1866,1868],"annotation",{"encoding":1867},"application\u002Fx-tex","E[Y\\mid X]",[1825,1870,1874,1909],{"className":1871,"ariaHidden":1873},[1872],"katex-html","true",[1825,1875,1878,1883,1889,1893,1897,1902,1906],{"className":1876},[1877],"base",[1825,1879],{"className":1880,"style":1882},[1881],"strut","height:1em;vertical-align:-0.25em;",[1825,1884,1846],{"className":1885,"style":1888},[1886,1887],"mord","mathnormal","margin-right:0.0576em;",[1825,1890,1851],{"className":1891},[1892],"mopen",[1825,1894,1854],{"className":1895,"style":1896},[1886,1887],"margin-right:0.2222em;",[1825,1898],{"className":1899,"style":1901},[1900],"mspace","margin-right:0.2778em;",[1825,1903,1857],{"className":1904},[1905],"mrel",[1825,1907],{"className":1908,"style":1901},[1900],[1825,1910,1912,1915,1919],{"className":1911},[1877],[1825,1913],{"className":1914,"style":1882},[1881],[1825,1916,1860],{"className":1917,"style":1918},[1886,1887],"margin-right:0.0785em;",[1825,1920,1863],{"className":1921},[1922],"mclose"," 与个体化处理效应 ",[1825,1925,1927,1973],{"className":1926},[1828],[1825,1928,1930],{"className":1929},[1832],[1834,1931,1932],{"xmlns":1836},[1838,1933,1934,1970],{},[1841,1935,1936,1938,1940,1942,1945,1949,1952,1955,1957,1959,1962,1964,1966,1968],{},[1844,1937,1846],{},[1848,1939,1851],{"stretchy":1850},[1844,1941,1854],{},[1848,1943,1944],{"stretchy":1850},"(",[1946,1947,1948],"mn",{},"1",[1848,1950,1951],{"stretchy":1850},")",[1848,1953,1954],{},"−",[1844,1956,1854],{},[1848,1958,1944],{"stretchy":1850},[1946,1960,1961],{},"0",[1848,1963,1951],{"stretchy":1850},[1848,1965,1857],{},[1844,1967,1860],{},[1848,1969,1863],{"stretchy":1850},[1865,1971,1972],{"encoding":1867},"E[Y(1)-Y(0)\\mid X]",[1825,1974,1976,2010,2037],{"className":1975,"ariaHidden":1873},[1872],[1825,1977,1979,1982,1985,1988,1991,1994,1997,2000,2003,2007],{"className":1978},[1877],[1825,1980],{"className":1981,"style":1882},[1881],[1825,1983,1846],{"className":1984,"style":1888},[1886,1887],[1825,1986,1851],{"className":1987},[1892],[1825,1989,1854],{"className":1990,"style":1896},[1886,1887],[1825,1992,1944],{"className":1993},[1892],[1825,1995,1948],{"className":1996},[1886],[1825,1998,1951],{"className":1999},[1922],[1825,2001],{"className":2002,"style":1896},[1900],[1825,2004,1954],{"className":2005},[2006],"mbin",[1825,2008],{"className":2009,"style":1896},[1900],[1825,2011,2013,2016,2019,2022,2025,2028,2031,2034],{"className":2012},[1877],[1825,2014],{"className":2015,"style":1882},[1881],[1825,2017,1854],{"className":2018,"style":1896},[1886,1887],[1825,2020,1944],{"className":2021},[1892],[1825,2023,1961],{"className":2024},[1886],[1825,2026,1951],{"className":2027},[1922],[1825,2029],{"className":2030,"style":1901},[1900],[1825,2032,1857],{"className":2033},[1905],[1825,2035],{"className":2036,"style":1901},[1900],[1825,2038,2040,2043,2046],{"className":2039},[1877],[1825,2041],{"className":2042,"style":1882},[1881],[1825,2044,1860],{"className":2045,"style":1918},[1886,1887],[1825,2047,1863],{"className":2048},[1922],"；",[1815,2051,2052],{},"写出从 ATE、CATE 到政策规则的完整决策链；",[1815,2054,2055],{},"解释从多个项目中选出最大估计值为何产生赢家诅咒；",[1815,2057,2058],{},"在论文阅读中同时审查识别、估计、推断、外部有效性与伦理边界。",[1805,2060,2062],{"id":2061},"案例一进入高度选择性大学的因果效应","案例一：进入高度选择性大学的因果效应",[2064,2065,2067],"h3",{"id":2066},"_1-先把处理定义清楚","1. 先把处理定义清楚",[1793,2069,2070,2071,2075],{},"Chetty、Deming 与 Friedman 的研究（2025 年在线发表，2026 年刊载于 ",[2072,2073,2074],"em",{},"Quarterly Journal of Economics","）结合多所高校的匿名录取资料、税务记录和标准化考试成绩，研究高度选择性私立大学的准入与就读后果。",[1793,2077,2078],{},"至少有三个不同问题：",[1812,2080,2081,2088,2094],{},[1815,2082,2083,2084,2087],{},"被 Ivy-Plus 大学",[1800,2085,2086],{},"录取","会怎样改变选择集合？",[1815,2089,2090,2093],{},[1800,2091,2092],{},"就读"," Ivy-Plus 而不是州旗舰公立大学会怎样改变结果？",[1815,2095,2096],{},"整体扩大名额或改变校友、非学术资历和体育特长偏好会怎样改变社会分配？",[1793,2098,2099],{},"这三个问题的处理、遵从和一般均衡效应都不同。论文的因果设计利用候补名单申请者录取决策中的特殊变动，目标不是简单比较两类毕业生的平均收入。",[2064,2101,2103],{"id":2102},"_2-选择偏误为何严重","2. 选择偏误为何严重",[1793,2105,2106],{},"朴素回归可写成：",[1825,2108,2111],{"className":2109},[2110],"katex-display",[1825,2112,2114,2197],{"className":2113},[1828],[1825,2115,2117],{"className":2116},[1832],[1834,2118,2120],{"xmlns":1836,"display":2119},"block",[1838,2121,2122,2194],{},[1841,2123,2124,2132,2135,2138,2141,2144,2147,2150,2152,2155,2158,2165,2167,2179,2182,2184,2191],{},[2125,2126,2127,2129],"msub",{},[1844,2128,1854],{},[1844,2130,2131],{},"i",[1848,2133,2134],{},"=",[1844,2136,2137],{},"α",[1848,2139,2140],{},"+",[1844,2142,2143],{},"τ",[1844,2145,2146],{},"A",[1844,2148,2149],{},"t",[1844,2151,2149],{},[1844,2153,2154],{},"e",[1844,2156,2157],{},"n",[2125,2159,2160,2163],{},[1844,2161,2162],{},"d",[1844,2164,2131],{},[1848,2166,2140],{},[2168,2169,2170,2172,2174],"msubsup",{},[1844,2171,1860],{},[1844,2173,2131],{},[1848,2175,2178],{"mathvariant":2176,"lspace":2177,"rspace":2177},"normal","0em","′",[1844,2180,2181],{},"β",[1848,2183,2140],{},[2125,2185,2186,2189],{},[1844,2187,2188],{},"u",[1844,2190,2131],{},[1844,2192,2193],{"mathvariant":2176},".",[1865,2195,2196],{"encoding":1867},"Y_i=\\alpha+\\tau Attend_i+X_i'\\beta+u_i.",[1825,2198,2200,2272,2292,2366,2444],{"className":2199,"ariaHidden":1873},[1872],[1825,2201,2203,2207,2263,2266,2269],{"className":2202},[1877],[1825,2204],{"className":2205,"style":2206},[1881],"height:0.8333em;vertical-align:-0.15em;",[1825,2208,2210,2213],{"className":2209},[1886],[1825,2211,1854],{"className":2212,"style":1896},[1886,1887],[1825,2214,2217],{"className":2215},[2216],"msupsub",[1825,2218,2222,2254],{"className":2219},[2220,2221],"vlist-t","vlist-t2",[1825,2223,2226,2249],{"className":2224},[2225],"vlist-r",[1825,2227,2231],{"className":2228,"style":2230},[2229],"vlist","height:0.3117em;",[1825,2232,2234,2239],{"style":2233},"top:-2.55em;margin-left:-0.2222em;margin-right:0.05em;",[1825,2235],{"className":2236,"style":2238},[2237],"pstrut","height:2.7em;",[1825,2240,2246],{"className":2241},[2242,2243,2244,2245],"sizing","reset-size6","size3","mtight",[1825,2247,2131],{"className":2248},[1886,1887,2245],[1825,2250,2253],{"className":2251},[2252],"vlist-s","​",[1825,2255,2257],{"className":2256},[2225],[1825,2258,2261],{"className":2259,"style":2260},[2229],"height:0.15em;",[1825,2262],{},[1825,2264],{"className":2265,"style":1901},[1900],[1825,2267,2134],{"className":2268},[1905],[1825,2270],{"className":2271,"style":1901},[1900],[1825,2273,2275,2279,2283,2286,2289],{"className":2274},[1877],[1825,2276],{"className":2277,"style":2278},[1881],"height:0.6667em;vertical-align:-0.0833em;",[1825,2280,2137],{"className":2281,"style":2282},[1886,1887],"margin-right:0.0037em;",[1825,2284],{"className":2285,"style":1896},[1900],[1825,2287,2140],{"className":2288},[2006],[1825,2290],{"className":2291,"style":1896},[1900],[1825,2293,2295,2299,2303,2306,2310,2313,2316,2357,2360,2363],{"className":2294},[1877],[1825,2296],{"className":2297,"style":2298},[1881],"height:0.8444em;vertical-align:-0.15em;",[1825,2300,2143],{"className":2301,"style":2302},[1886,1887],"margin-right:0.1132em;",[1825,2304,2146],{"className":2305},[1886,1887],[1825,2307,2309],{"className":2308},[1886,1887],"tt",[1825,2311,2154],{"className":2312},[1886,1887],[1825,2314,2157],{"className":2315},[1886,1887],[1825,2317,2319,2322],{"className":2318},[1886],[1825,2320,2162],{"className":2321},[1886,1887],[1825,2323,2325],{"className":2324},[2216],[1825,2326,2328,2349],{"className":2327},[2220,2221],[1825,2329,2331,2346],{"className":2330},[2225],[1825,2332,2334],{"className":2333,"style":2230},[2229],[1825,2335,2337,2340],{"style":2336},"top:-2.55em;margin-left:0em;margin-right:0.05em;",[1825,2338],{"className":2339,"style":2238},[2237],[1825,2341,2343],{"className":2342},[2242,2243,2244,2245],[1825,2344,2131],{"className":2345},[1886,1887,2245],[1825,2347,2253],{"className":2348},[2252],[1825,2350,2352],{"className":2351},[2225],[1825,2353,2355],{"className":2354,"style":2260},[2229],[1825,2356],{},[1825,2358],{"className":2359,"style":1896},[1900],[1825,2361,2140],{"className":2362},[2006],[1825,2364],{"className":2365,"style":1896},[1900],[1825,2367,2369,2373,2431,2435,2438,2441],{"className":2368},[1877],[1825,2370],{"className":2371,"style":2372},[1881],"height:1.0489em;vertical-align:-0.247em;",[1825,2374,2376,2379],{"className":2375},[1886],[1825,2377,1860],{"className":2378,"style":1918},[1886,1887],[1825,2380,2382],{"className":2381},[2216],[1825,2383,2385,2422],{"className":2384},[2220,2221],[1825,2386,2388,2419],{"className":2387},[2225],[1825,2389,2392,2404],{"className":2390,"style":2391},[2229],"height:0.8019em;",[1825,2393,2395,2398],{"style":2394},"top:-2.453em;margin-left:-0.0785em;margin-right:0.05em;",[1825,2396],{"className":2397,"style":2238},[2237],[1825,2399,2401],{"className":2400},[2242,2243,2244,2245],[1825,2402,2131],{"className":2403},[1886,1887,2245],[1825,2405,2407,2410],{"style":2406},"top:-3.113em;margin-right:0.05em;",[1825,2408],{"className":2409,"style":2238},[2237],[1825,2411,2413],{"className":2412},[2242,2243,2244,2245],[1825,2414,2416],{"className":2415},[1886,2245],[1825,2417,2178],{"className":2418},[1886,2245],[1825,2420,2253],{"className":2421},[2252],[1825,2423,2425],{"className":2424},[2225],[1825,2426,2429],{"className":2427,"style":2428},[2229],"height:0.247em;",[1825,2430],{},[1825,2432,2181],{"className":2433,"style":2434},[1886,1887],"margin-right:0.0528em;",[1825,2436],{"className":2437,"style":1896},[1900],[1825,2439,2140],{"className":2440},[2006],[1825,2442],{"className":2443,"style":1896},[1900],[1825,2445,2447,2451,2491],{"className":2446},[1877],[1825,2448],{"className":2449,"style":2450},[1881],"height:0.5806em;vertical-align:-0.15em;",[1825,2452,2454,2457],{"className":2453},[1886],[1825,2455,2188],{"className":2456},[1886,1887],[1825,2458,2460],{"className":2459},[2216],[1825,2461,2463,2483],{"className":2462},[2220,2221],[1825,2464,2466,2480],{"className":2465},[2225],[1825,2467,2469],{"className":2468,"style":2230},[2229],[1825,2470,2471,2474],{"style":2336},[1825,2472],{"className":2473,"style":2238},[2237],[1825,2475,2477],{"className":2476},[2242,2243,2244,2245],[1825,2478,2131],{"className":2479},[1886,1887,2245],[1825,2481,2253],{"className":2482},[2252],[1825,2484,2486],{"className":2485},[2225],[1825,2487,2489],{"className":2488,"style":2260},[2229],[1825,2490],{},[1825,2492,2193],{"className":2493},[1886],[1793,2495,2496,2497,2589,2590,2620],{},"即使控制考试成绩，",[1825,2498,2500,2528],{"className":2499},[1828],[1825,2501,2503],{"className":2502},[1832],[1834,2504,2505],{"xmlns":1836},[1838,2506,2507,2525],{},[1841,2508,2509,2511,2513,2515,2517,2519],{},[1844,2510,2146],{},[1844,2512,2149],{},[1844,2514,2149],{},[1844,2516,2154],{},[1844,2518,2157],{},[2125,2520,2521,2523],{},[1844,2522,2162],{},[1844,2524,2131],{},[1865,2526,2527],{"encoding":1867},"Attend_i",[1825,2529,2531],{"className":2530,"ariaHidden":1873},[1872],[1825,2532,2534,2537,2540,2543,2546,2549],{"className":2533},[1877],[1825,2535],{"className":2536,"style":2298},[1881],[1825,2538,2146],{"className":2539},[1886,1887],[1825,2541,2309],{"className":2542},[1886,1887],[1825,2544,2154],{"className":2545},[1886,1887],[1825,2547,2157],{"className":2548},[1886,1887],[1825,2550,2552,2555],{"className":2551},[1886],[1825,2553,2162],{"className":2554},[1886,1887],[1825,2556,2558],{"className":2557},[2216],[1825,2559,2561,2581],{"className":2560},[2220,2221],[1825,2562,2564,2578],{"className":2563},[2225],[1825,2565,2567],{"className":2566,"style":2230},[2229],[1825,2568,2569,2572],{"style":2336},[1825,2570],{"className":2571,"style":2238},[2237],[1825,2573,2575],{"className":2574},[2242,2243,2244,2245],[1825,2576,2131],{"className":2577},[1886,1887,2245],[1825,2579,2253],{"className":2580},[2252],[1825,2582,2584],{"className":2583},[2225],[1825,2585,2587],{"className":2586,"style":2260},[2229],[1825,2588],{}," 仍可能与申请组合、家庭资源、推荐质量、职业偏好和未测能力相关。",[1825,2591,2593,2607],{"className":2592},[1828],[1825,2594,2596],{"className":2595},[1832],[1834,2597,2598],{"xmlns":1836},[1838,2599,2600,2604],{},[1841,2601,2602],{},[1844,2603,2143],{},[1865,2605,2606],{"encoding":1867},"\\tau",[1825,2608,2610],{"className":2609,"ariaHidden":1873},[1872],[1825,2611,2613,2617],{"className":2612},[1877],[1825,2614],{"className":2615,"style":2616},[1881],"height:0.4306em;",[1825,2618,2143],{"className":2619,"style":2302},[1886,1887]," 因而混合学校作用与入学前差异。",[1793,2622,2623],{},"更可信的设计要寻找一个改变就读概率、但在适当条件下不直接改变结果的变动。用工具变量语言表示：",[1825,2625,2627],{"className":2626},[2110],[1825,2628,2630,2711],{"className":2629},[1828],[1825,2631,2633],{"className":2632},[1832],[1834,2634,2635],{"xmlns":1836,"display":2119},[1838,2636,2637,2708],{},[1841,2638,2639,2641,2643,2645,2647,2649,2655,2657,2664,2666,2672,2679,2681,2689,2696,2698,2705],{},[1844,2640,2146],{},[1844,2642,2149],{},[1844,2644,2149],{},[1844,2646,2154],{},[1844,2648,2157],{},[2125,2650,2651,2653],{},[1844,2652,2162],{},[1844,2654,2131],{},[1848,2656,2134],{},[2125,2658,2659,2662],{},[1844,2660,2661],{},"π",[1946,2663,1961],{},[1848,2665,2140],{},[2125,2667,2668,2670],{},[1844,2669,2661],{},[1946,2671,1948],{},[2125,2673,2674,2677],{},[1844,2675,2676],{},"Z",[1844,2678,2131],{},[1848,2680,2140],{},[2168,2682,2683,2685,2687],{},[1844,2684,1860],{},[1844,2686,2131],{},[1848,2688,2178],{"mathvariant":2176,"lspace":2177,"rspace":2177},[2125,2690,2691,2693],{},[1844,2692,2661],{},[1946,2694,2695],{},"2",[1848,2697,2140],{},[2125,2699,2700,2703],{},[1844,2701,2702],{},"v",[1844,2704,2131],{},[1848,2706,2707],{"separator":1873},",",[1865,2709,2710],{"encoding":1867},"Attend_i=\\pi_0+\\pi_1 Z_i+X_i'\\pi_2+v_i,",[1825,2712,2714,2781,2840,2937,3046],{"className":2713,"ariaHidden":1873},[1872],[1825,2715,2717,2720,2723,2726,2729,2732,2772,2775,2778],{"className":2716},[1877],[1825,2718],{"className":2719,"style":2298},[1881],[1825,2721,2146],{"className":2722},[1886,1887],[1825,2724,2309],{"className":2725},[1886,1887],[1825,2727,2154],{"className":2728},[1886,1887],[1825,2730,2157],{"className":2731},[1886,1887],[1825,2733,2735,2738],{"className":2734},[1886],[1825,2736,2162],{"className":2737},[1886,1887],[1825,2739,2741],{"className":2740},[2216],[1825,2742,2744,2764],{"className":2743},[2220,2221],[1825,2745,2747,2761],{"className":2746},[2225],[1825,2748,2750],{"className":2749,"style":2230},[2229],[1825,2751,2752,2755],{"style":2336},[1825,2753],{"className":2754,"style":2238},[2237],[1825,2756,2758],{"className":2757},[2242,2243,2244,2245],[1825,2759,2131],{"className":2760},[1886,1887,2245],[1825,2762,2253],{"className":2763},[2252],[1825,2765,2767],{"className":2766},[2225],[1825,2768,2770],{"className":2769,"style":2260},[2229],[1825,2771],{},[1825,2773],{"className":2774,"style":1901},[1900],[1825,2776,2134],{"className":2777},[1905],[1825,2779],{"className":2780,"style":1901},[1900],[1825,2782,2784,2788,2831,2834,2837],{"className":2783},[1877],[1825,2785],{"className":2786,"style":2787},[1881],"height:0.7333em;vertical-align:-0.15em;",[1825,2789,2791,2795],{"className":2790},[1886],[1825,2792,2661],{"className":2793,"style":2794},[1886,1887],"margin-right:0.0359em;",[1825,2796,2798],{"className":2797},[2216],[1825,2799,2801,2823],{"className":2800},[2220,2221],[1825,2802,2804,2820],{"className":2803},[2225],[1825,2805,2808],{"className":2806,"style":2807},[2229],"height:0.3011em;",[1825,2809,2811,2814],{"style":2810},"top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;",[1825,2812],{"className":2813,"style":2238},[2237],[1825,2815,2817],{"className":2816},[2242,2243,2244,2245],[1825,2818,1961],{"className":2819},[1886,2245],[1825,2821,2253],{"className":2822},[2252],[1825,2824,2826],{"className":2825},[2225],[1825,2827,2829],{"className":2828,"style":2260},[2229],[1825,2830],{},[1825,2832],{"className":2833,"style":1896},[1900],[1825,2835,2140],{"className":2836},[2006],[1825,2838],{"className":2839,"style":1896},[1900],[1825,2841,2843,2846,2886,2928,2931,2934],{"className":2842},[1877],[1825,2844],{"className":2845,"style":2206},[1881],[1825,2847,2849,2852],{"className":2848},[1886],[1825,2850,2661],{"className":2851,"style":2794},[1886,1887],[1825,2853,2855],{"className":2854},[2216],[1825,2856,2858,2878],{"className":2857},[2220,2221],[1825,2859,2861,2875],{"className":2860},[2225],[1825,2862,2864],{"className":2863,"style":2807},[2229],[1825,2865,2866,2869],{"style":2810},[1825,2867],{"className":2868,"style":2238},[2237],[1825,2870,2872],{"className":2871},[2242,2243,2244,2245],[1825,2873,1948],{"className":2874},[1886,2245],[1825,2876,2253],{"className":2877},[2252],[1825,2879,2881],{"className":2880},[2225],[1825,2882,2884],{"className":2883,"style":2260},[2229],[1825,2885],{},[1825,2887,2889,2893],{"className":2888},[1886],[1825,2890,2676],{"className":2891,"style":2892},[1886,1887],"margin-right:0.0715em;",[1825,2894,2896],{"className":2895},[2216],[1825,2897,2899,2920],{"className":2898},[2220,2221],[1825,2900,2902,2917],{"className":2901},[2225],[1825,2903,2905],{"className":2904,"style":2230},[2229],[1825,2906,2908,2911],{"style":2907},"top:-2.55em;margin-left:-0.0715em;margin-right:0.05em;",[1825,2909],{"className":2910,"style":2238},[2237],[1825,2912,2914],{"className":2913},[2242,2243,2244,2245],[1825,2915,2131],{"className":2916},[1886,1887,2245],[1825,2918,2253],{"className":2919},[2252],[1825,2921,2923],{"className":2922},[2225],[1825,2924,2926],{"className":2925,"style":2260},[2229],[1825,2927],{},[1825,2929],{"className":2930,"style":1896},[1900],[1825,2932,2140],{"className":2933},[2006],[1825,2935],{"className":2936,"style":1896},[1900],[1825,2938,2940,2943,2997,3037,3040,3043],{"className":2939},[1877],[1825,2941],{"className":2942,"style":2372},[1881],[1825,2944,2946,2949],{"className":2945},[1886],[1825,2947,1860],{"className":2948,"style":1918},[1886,1887],[1825,2950,2952],{"className":2951},[2216],[1825,2953,2955,2989],{"className":2954},[2220,2221],[1825,2956,2958,2986],{"className":2957},[2225],[1825,2959,2961,2972],{"className":2960,"style":2391},[2229],[1825,2962,2963,2966],{"style":2394},[1825,2964],{"className":2965,"style":2238},[2237],[1825,2967,2969],{"className":2968},[2242,2243,2244,2245],[1825,2970,2131],{"className":2971},[1886,1887,2245],[1825,2973,2974,2977],{"style":2406},[1825,2975],{"className":2976,"style":2238},[2237],[1825,2978,2980],{"className":2979},[2242,2243,2244,2245],[1825,2981,2983],{"className":2982},[1886,2245],[1825,2984,2178],{"className":2985},[1886,2245],[1825,2987,2253],{"className":2988},[2252],[1825,2990,2992],{"className":2991},[2225],[1825,2993,2995],{"className":2994,"style":2428},[2229],[1825,2996],{},[1825,2998,3000,3003],{"className":2999},[1886],[1825,3001,2661],{"className":3002,"style":2794},[1886,1887],[1825,3004,3006],{"className":3005},[2216],[1825,3007,3009,3029],{"className":3008},[2220,2221],[1825,3010,3012,3026],{"className":3011},[2225],[1825,3013,3015],{"className":3014,"style":2807},[2229],[1825,3016,3017,3020],{"style":2810},[1825,3018],{"className":3019,"style":2238},[2237],[1825,3021,3023],{"className":3022},[2242,2243,2244,2245],[1825,3024,2695],{"className":3025},[1886,2245],[1825,3027,2253],{"className":3028},[2252],[1825,3030,3032],{"className":3031},[2225],[1825,3033,3035],{"className":3034,"style":2260},[2229],[1825,3036],{},[1825,3038],{"className":3039,"style":1896},[1900],[1825,3041,2140],{"className":3042},[2006],[1825,3044],{"className":3045,"style":1896},[1900],[1825,3047,3049,3053,3093],{"className":3048},[1877],[1825,3050],{"className":3051,"style":3052},[1881],"height:0.625em;vertical-align:-0.1944em;",[1825,3054,3056,3059],{"className":3055},[1886],[1825,3057,2702],{"className":3058,"style":2794},[1886,1887],[1825,3060,3062],{"className":3061},[2216],[1825,3063,3065,3085],{"className":3064},[2220,2221],[1825,3066,3068,3082],{"className":3067},[2225],[1825,3069,3071],{"className":3070,"style":2230},[2229],[1825,3072,3073,3076],{"style":2810},[1825,3074],{"className":3075,"style":2238},[2237],[1825,3077,3079],{"className":3078},[2242,2243,2244,2245],[1825,3080,2131],{"className":3081},[1886,1887,2245],[1825,3083,2253],{"className":3084},[2252],[1825,3086,3088],{"className":3087},[2225],[1825,3089,3091],{"className":3090,"style":2260},[2229],[1825,3092],{},[1825,3094,2707],{"className":3095},[3096],"mpunct",[1793,3098,3099,3100,3170],{},"其中 ",[1825,3101,3103,3121],{"className":3102},[1828],[1825,3104,3106],{"className":3105},[1832],[1834,3107,3108],{"xmlns":1836},[1838,3109,3110,3118],{},[1841,3111,3112],{},[2125,3113,3114,3116],{},[1844,3115,2676],{},[1844,3117,2131],{},[1865,3119,3120],{"encoding":1867},"Z_i",[1825,3122,3124],{"className":3123,"ariaHidden":1873},[1872],[1825,3125,3127,3130],{"className":3126},[1877],[1825,3128],{"className":3129,"style":2206},[1881],[1825,3131,3133,3136],{"className":3132},[1886],[1825,3134,2676],{"className":3135,"style":2892},[1886,1887],[1825,3137,3139],{"className":3138},[2216],[1825,3140,3142,3162],{"className":3141},[2220,2221],[1825,3143,3145,3159],{"className":3144},[2225],[1825,3146,3148],{"className":3147,"style":2230},[2229],[1825,3149,3150,3153],{"style":2907},[1825,3151],{"className":3152,"style":2238},[2237],[1825,3154,3156],{"className":3155},[2242,2243,2244,2245],[1825,3157,2131],{"className":3158},[1886,1887,2245],[1825,3160,2253],{"className":3161},[2252],[1825,3163,3165],{"className":3164},[2225],[1825,3166,3168],{"className":3167,"style":2260},[2229],[1825,3169],{}," 来自候补录取决策中的准实验变动。若相关性、排除限制、独立性和单调性成立，IV 识别的是被该变动改变就读选择者的局部平均处理效应，而不是所有学生的 ATE。",[2064,3172,3174],{"id":3173},"_3-论文报告了什么","3. 论文报告了什么",[1793,3176,3177],{},"论文首先记录：在考试成绩相近的申请者中，最高收入 1% 家庭的子女进入 Ivy-Plus 的概率仍超过中产家庭子女的两倍；作者把差距分解为申请、录取和入学三个环节，并将录取优势与校友子女偏好、非学术资历权重和体育招募联系起来。",[1793,3179,3180,3181,3184],{},"在论文的局部因果设计与特定比较下，就读 Ivy-Plus 而不是平均州旗舰公立大学，使进入收入分布最高 1% 的概率相对提高约 50%，进入顶尖研究生院的概率接近翻倍，在高声望企业工作的概率接近三倍。这些是",[1800,3182,3183],{},"相对变化","，不是增加 50、100 或 200 个百分点。",[2064,3186,3188],{"id":3187},"_4-证据边界","4. 证据边界",[3190,3191,3192,3195,3198,3201,3204],"ul",{},[1815,3193,3194],{},"估计与候补名单附近、会因录取变动而改变就读选择的人群最直接相关。",[1815,3196,3197],{},"“Ivy-Plus 相对州旗舰”不是“大学相对不读大学”。",[1815,3199,3200],{},"顶尖收入、研究生院和企业声望是特定结果，不等同于终身福利、幸福或社会贡献。",[1815,3202,3203],{},"扩大名额可能改变同伴、师资、课程与劳动力市场信号，因此局部效应不能机械乘以新增学生人数。",[1815,3205,3206],{},"行政数据提高测量质量，但仍不能替代对排除限制和制度细节的论证。",[2064,3208,3210],{"id":3209},"_5-分层讨论","5. 分层讨论",[1793,3212,3213,3214,3312,3313,3341,3342,3370],{},"本科生应能画出 ",[1825,3215,3217,3250],{"className":3216},[1828],[1825,3218,3220],{"className":3219},[1832],[1834,3221,3222],{"xmlns":1836},[1838,3223,3224,3247],{},[1841,3225,3226,3228,3231,3233,3235,3237,3239,3241,3243,3245],{},[1844,3227,2676],{},[1848,3229,3230],{},"→",[1844,3232,2146],{},[1844,3234,2149],{},[1844,3236,2149],{},[1844,3238,2154],{},[1844,3240,2157],{},[1844,3242,2162],{},[1848,3244,3230],{},[1844,3246,1854],{},[1865,3248,3249],{"encoding":1867},"Z\\rightarrow Attend\\rightarrow Y",[1825,3251,3253,3272,3303],{"className":3252,"ariaHidden":1873},[1872],[1825,3254,3256,3260,3263,3266,3269],{"className":3255},[1877],[1825,3257],{"className":3258,"style":3259},[1881],"height:0.6833em;",[1825,3261,2676],{"className":3262,"style":2892},[1886,1887],[1825,3264],{"className":3265,"style":1901},[1900],[1825,3267,3230],{"className":3268},[1905],[1825,3270],{"className":3271,"style":1901},[1900],[1825,3273,3275,3279,3282,3285,3288,3291,3294,3297,3300],{"className":3274},[1877],[1825,3276],{"className":3277,"style":3278},[1881],"height:0.6944em;",[1825,3280,2146],{"className":3281},[1886,1887],[1825,3283,2309],{"className":3284},[1886,1887],[1825,3286,2154],{"className":3287},[1886,1887],[1825,3289,2157],{"className":3290},[1886,1887],[1825,3292,2162],{"className":3293},[1886,1887],[1825,3295],{"className":3296,"style":1901},[1900],[1825,3298,3230],{"className":3299},[1905],[1825,3301],{"className":3302,"style":1901},[1900],[1825,3304,3306,3309],{"className":3305},[1877],[1825,3307],{"className":3308,"style":3259},[1881],[1825,3310,1854],{"className":3311,"style":1896},[1886,1887]," 的因果图，并列出 ",[1825,3314,3316,3329],{"className":3315},[1828],[1825,3317,3319],{"className":3318},[1832],[1834,3320,3321],{"xmlns":1836},[1838,3322,3323,3327],{},[1841,3324,3325],{},[1844,3326,2676],{},[1865,3328,2676],{"encoding":1867},[1825,3330,3332],{"className":3331,"ariaHidden":1873},[1872],[1825,3333,3335,3338],{"className":3334},[1877],[1825,3336],{"className":3337,"style":3259},[1881],[1825,3339,2676],{"className":3340,"style":2892},[1886,1887]," 可能直接影响 ",[1825,3343,3345,3358],{"className":3344},[1828],[1825,3346,3348],{"className":3347},[1832],[1834,3349,3350],{"xmlns":1836},[1838,3351,3352,3356],{},[1841,3353,3354],{},[1844,3355,1854],{},[1865,3357,1854],{"encoding":1867},[1825,3359,3361],{"className":3360,"ariaHidden":1873},[1872],[1825,3362,3364,3367],{"className":3363},[1877],[1825,3365],{"className":3366,"style":3259},[1881],[1825,3368,1854],{"className":3369,"style":1896},[1886,1887]," 的路径。研究生还应讨论多校申请、处理版本、非遵从、溢出效应、边际申请者构成和政策外推权重。",[2064,3372,3374],{"id":3373},"_6-同题异设计公立大学录取断点","6. 同题异设计：公立大学录取断点",[1793,3376,3377,3378,3381],{},"Mountjoy（2026）使用得州 35 所公立大学数百个 SAT\u002FACT 录取断点，将刚好越过与刚好未越过各校门槛的申请者进行比较。门槛使录取概率平均跳升 27 个百分点，并使就读目标大学的概率提高 15 个百分点，因此这是 ",[1800,3379,3380],{},"fuzzy RDD","：阈值资格是工具，实际就读是内生处理。",[1793,3383,3384],{},"论文估计，典型边际录取者在四年制大学多接受约一年教育，取得学士学位的概率提高 12 个百分点，长期收入提高约 8%。成本—收益计算进一步报告学生、社会和政府预算视角下的内部收益率分别约为 26%、16% 和 7%。这些数字都对应论文的特定边际申请者、追踪期与成本假设。",[1793,3386,3387],{},"这个对照有三层教学价值：",[3389,3390,3391,3407],"table",{},[3392,3393,3394],"thead",{},[3395,3396,3397,3401,3404],"tr",{},[3398,3399,3400],"th",{},"比较",[3398,3402,3403],{},"Ivy-Plus 候补名单设计",[3398,3405,3406],{},"公立大学录取断点",[3408,3409,3410,3422,3433,3444],"tbody",{},[3395,3411,3412,3416,3419],{},[3413,3414,3415],"td",{},"外生变动",[3413,3417,3418],{},"候补录取决策中的特殊变动",[3413,3420,3421],{},"考试分数刚好越过校级门槛",[3395,3423,3424,3427,3430],{},[3413,3425,3426],{},"主要处理",[3413,3428,3429],{},"就读 Ivy-Plus 而非州旗舰",[3413,3431,3432],{},"因录取门槛而进入目标公立大学",[3395,3434,3435,3438,3441],{},[3413,3436,3437],{},"局部人群",[3413,3439,3440],{},"会被候补录取改变去向者",[3413,3442,3443],{},"门槛附近的录取顺从者",[3395,3445,3446,3449,3452],{},[3413,3447,3448],{},"政策含义",[3413,3450,3451],{},"选择性私立大学的录取与分配",[3413,3453,3454],{},"公立大学边际扩招的收益与成本",[1793,3456,3457],{},"两项估计不回答同一个 ATE。即使都研究“大学回报”，工具、替代状态与顺从者不同，就不能直接比较数字大小。Mountjoy 还区分“是否进入任一四年制大学”的广延边际与“转向更具选择性学校”的集约边际；这一分解说明处理版本本身就是 estimand 的一部分。",[1805,3459,3461],{"id":3460},"案例二从谁风险高到谁会因治疗而受益","案例二：从“谁风险高”到“谁会因治疗而受益”",[2064,3463,3465],{"id":3464},"_1-预测与因果不是同一个任务","1. 预测与因果不是同一个任务",[1793,3467,3468,3469,3472],{},"Feuerriegel 等（2024）在 ",[2072,3470,3471],{},"Nature Medicine"," 的综述讨论因果机器学习如何预测处理后的疗效与毒性，并给出从问题定义、数据、识别、估计到临床转化的工作流。",[1793,3474,3475],{},"普通风险模型估计：",[1825,3477,3479],{"className":3478},[2110],[1825,3480,3482,3524],{"className":3481},[1828],[1825,3483,3485],{"className":3484},[1832],[1834,3486,3487],{"xmlns":1836,"display":2119},[1838,3488,3489,3521],{},[1841,3490,3491,3494,3496,3499,3501,3503,3505,3507,3509,3511,3513,3515,3517,3519],{},[1844,3492,3493],{},"μ",[1848,3495,1944],{"stretchy":1850},[1844,3497,3498],{},"x",[1848,3500,1951],{"stretchy":1850},[1848,3502,2134],{},[1844,3504,1846],{},[1848,3506,1851],{"stretchy":1850},[1844,3508,1854],{},[1848,3510,1857],{},[1844,3512,1860],{},[1848,3514,2134],{},[1844,3516,3498],{},[1848,3518,1863],{"stretchy":1850},[1844,3520,2193],{"mathvariant":2176},[1865,3522,3523],{"encoding":1867},"\\mu(x)=E[Y\\mid X=x].",[1825,3525,3527,3554,3578,3596],{"className":3526,"ariaHidden":1873},[1872],[1825,3528,3530,3533,3536,3539,3542,3545,3548,3551],{"className":3529},[1877],[1825,3531],{"className":3532,"style":1882},[1881],[1825,3534,3493],{"className":3535},[1886,1887],[1825,3537,1944],{"className":3538},[1892],[1825,3540,3498],{"className":3541},[1886,1887],[1825,3543,1951],{"className":3544},[1922],[1825,3546],{"className":3547,"style":1901},[1900],[1825,3549,2134],{"className":3550},[1905],[1825,3552],{"className":3553,"style":1901},[1900],[1825,3555,3557,3560,3563,3566,3569,3572,3575],{"className":3556},[1877],[1825,3558],{"className":3559,"style":1882},[1881],[1825,3561,1846],{"className":3562,"style":1888},[1886,1887],[1825,3564,1851],{"className":3565},[1892],[1825,3567,1854],{"className":3568,"style":1896},[1886,1887],[1825,3570],{"className":3571,"style":1901},[1900],[1825,3573,1857],{"className":3574},[1905],[1825,3576],{"className":3577,"style":1901},[1900],[1825,3579,3581,3584,3587,3590,3593],{"className":3580},[1877],[1825,3582],{"className":3583,"style":3259},[1881],[1825,3585,1860],{"className":3586,"style":1918},[1886,1887],[1825,3588],{"className":3589,"style":1901},[1900],[1825,3591,2134],{"className":3592},[1905],[1825,3594],{"className":3595,"style":1901},[1900],[1825,3597,3599,3602,3605,3608],{"className":3598},[1877],[1825,3600],{"className":3601,"style":1882},[1881],[1825,3603,3498],{"className":3604},[1886,1887],[1825,3606,1863],{"className":3607},[1922],[1825,3609,2193],{"className":3610},[1886],[1793,3612,3613],{},"个体化治疗需要比较两个不能同时观察的潜在结果：",[1825,3615,3617],{"className":3616},[2110],[1825,3618,3620,3676],{"className":3619},[1828],[1825,3621,3623],{"className":3622},[1832],[1834,3624,3625],{"xmlns":1836,"display":2119},[1838,3626,3627,3673],{},[1841,3628,3629,3631,3633,3635,3637,3639,3641,3643,3645,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671],{},[1844,3630,2143],{},[1848,3632,1944],{"stretchy":1850},[1844,3634,3498],{},[1848,3636,1951],{"stretchy":1850},[1848,3638,2134],{},[1844,3640,1846],{},[1848,3642,1851],{"stretchy":1850},[1844,3644,1854],{},[1848,3646,1944],{"stretchy":1850},[1946,3648,1948],{},[1848,3650,1951],{"stretchy":1850},[1848,3652,1954],{},[1844,3654,1854],{},[1848,3656,1944],{"stretchy":1850},[1946,3658,1961],{},[1848,3660,1951],{"stretchy":1850},[1848,3662,1857],{},[1844,3664,1860],{},[1848,3666,2134],{},[1844,3668,3498],{},[1848,3670,1863],{"stretchy":1850},[1844,3672,2193],{"mathvariant":2176},[1865,3674,3675],{"encoding":1867},"\\tau(x)=E[Y(1)-Y(0)\\mid X=x].",[1825,3677,3679,3706,3739,3766,3784],{"className":3678,"ariaHidden":1873},[1872],[1825,3680,3682,3685,3688,3691,3694,3697,3700,3703],{"className":3681},[1877],[1825,3683],{"className":3684,"style":1882},[1881],[1825,3686,2143],{"className":3687,"style":2302},[1886,1887],[1825,3689,1944],{"className":3690},[1892],[1825,3692,3498],{"className":3693},[1886,1887],[1825,3695,1951],{"className":3696},[1922],[1825,3698],{"className":3699,"style":1901},[1900],[1825,3701,2134],{"className":3702},[1905],[1825,3704],{"className":3705,"style":1901},[1900],[1825,3707,3709,3712,3715,3718,3721,3724,3727,3730,3733,3736],{"className":3708},[1877],[1825,3710],{"className":3711,"style":1882},[1881],[1825,3713,1846],{"className":3714,"style":1888},[1886,1887],[1825,3716,1851],{"className":3717},[1892],[1825,3719,1854],{"className":3720,"style":1896},[1886,1887],[1825,3722,1944],{"className":3723},[1892],[1825,3725,1948],{"className":3726},[1886],[1825,3728,1951],{"className":3729},[1922],[1825,3731],{"className":3732,"style":1896},[1900],[1825,3734,1954],{"className":3735},[2006],[1825,3737],{"className":3738,"style":1896},[1900],[1825,3740,3742,3745,3748,3751,3754,3757,3760,3763],{"className":3741},[1877],[1825,3743],{"className":3744,"style":1882},[1881],[1825,3746,1854],{"className":3747,"style":1896},[1886,1887],[1825,3749,1944],{"className":3750},[1892],[1825,3752,1961],{"className":3753},[1886],[1825,3755,1951],{"className":3756},[1922],[1825,3758],{"className":3759,"style":1901},[1900],[1825,3761,1857],{"className":3762},[1905],[1825,3764],{"className":3765,"style":1901},[1900],[1825,3767,3769,3772,3775,3778,3781],{"className":3768},[1877],[1825,3770],{"className":3771,"style":3259},[1881],[1825,3773,1860],{"className":3774,"style":1918},[1886,1887],[1825,3776],{"className":3777,"style":1901},[1900],[1825,3779,2134],{"className":3780},[1905],[1825,3782],{"className":3783,"style":1901},[1900],[1825,3785,3787,3790,3793,3796],{"className":3786},[1877],[1825,3788],{"className":3789,"style":1882},[1881],[1825,3791,3498],{"className":3792},[1886,1887],[1825,3794,1863],{"className":3795},[1922],[1825,3797,2193],{"className":3798},[1886],[1793,3800,3801],{},"一个患者在不治疗时风险很高，不代表治疗效果最大。风险预测依赖结果相关特征；处理效应异质性则依赖这些特征是否改变处理与结果之间的差异。",[2064,3803,3805],{"id":3804},"_2-cate-如何进入政策规则","2. CATE 如何进入政策规则",[1793,3807,3808,3809,3854],{},"若处理成本或伤害以结果单位表示为 ",[1825,3810,3812,3833],{"className":3811},[1828],[1825,3813,3815],{"className":3814},[1832],[1834,3816,3817],{"xmlns":1836},[1838,3818,3819,3830],{},[1841,3820,3821,3824,3826,3828],{},[1844,3822,3823],{},"c",[1848,3825,1944],{"stretchy":1850},[1844,3827,3498],{},[1848,3829,1951],{"stretchy":1850},[1865,3831,3832],{"encoding":1867},"c(x)",[1825,3834,3836],{"className":3835,"ariaHidden":1873},[1872],[1825,3837,3839,3842,3845,3848,3851],{"className":3838},[1877],[1825,3840],{"className":3841,"style":1882},[1881],[1825,3843,3823],{"className":3844},[1886,1887],[1825,3846,1944],{"className":3847},[1892],[1825,3849,3498],{"className":3850},[1886,1887],[1825,3852,1951],{"className":3853},[1922],"，一个简单规则是：",[1825,3856,3858],{"className":3857},[2110],[1825,3859,3861,3919],{"className":3860},[1828],[1825,3862,3864],{"className":3863},[1832],[1834,3865,3866],{"xmlns":1836,"display":2119},[1838,3867,3868,3916],{},[1841,3869,3870,3872,3874,3876,3878,3880,3883,3886,3894,3896,3898,3900,3903,3905,3907,3909,3911,3914],{},[1844,3871,2162],{},[1848,3873,1944],{"stretchy":1850},[1844,3875,3498],{},[1848,3877,1951],{"stretchy":1850},[1848,3879,2134],{},[1946,3881,1948],{"mathvariant":3882},"bold",[1848,3884,3885],{"stretchy":1850},"{",[3887,3888,3889,3891],"mover",{"accent":1873},[1844,3890,2143],{},[1848,3892,3893],{"stretchy":1873},"^",[1848,3895,1944],{"stretchy":1850},[1844,3897,3498],{},[1848,3899,1951],{"stretchy":1850},[1848,3901,3902],{},">",[1844,3904,3823],{},[1848,3906,1944],{"stretchy":1850},[1844,3908,3498],{},[1848,3910,1951],{"stretchy":1850},[1848,3912,3913],{"stretchy":1850},"}",[1844,3915,2193],{"mathvariant":2176},[1865,3917,3918],{"encoding":1867},"d(x)=\\mathbf 1\\{\\widehat\\tau(x)>c(x)\\}.",[1825,3920,3922,3949,4027],{"className":3921,"ariaHidden":1873},[1872],[1825,3923,3925,3928,3931,3934,3937,3940,3943,3946],{"className":3924},[1877],[1825,3926],{"className":3927,"style":1882},[1881],[1825,3929,2162],{"className":3930},[1886,1887],[1825,3932,1944],{"className":3933},[1892],[1825,3935,3498],{"className":3936},[1886,1887],[1825,3938,1951],{"className":3939},[1922],[1825,3941],{"className":3942,"style":1901},[1900],[1825,3944,2134],{"className":3945},[1905],[1825,3947],{"className":3948,"style":1901},[1900],[1825,3950,3952,3955,3959,3962,4009,4012,4015,4018,4021,4024],{"className":3951},[1877],[1825,3953],{"className":3954,"style":1882},[1881],[1825,3956,1948],{"className":3957},[1886,3958],"mathbf",[1825,3960,3885],{"className":3961},[1892],[1825,3963,3966],{"className":3964},[1886,3965],"accent",[1825,3967,3969],{"className":3968},[2220],[1825,3970,3972],{"className":3971},[2225],[1825,3973,3976,3986],{"className":3974,"style":3975},[2229],"height:0.6706em;",[1825,3977,3979,3983],{"style":3978},"top:-3em;",[1825,3980],{"className":3981,"style":3982},[2237],"height:3em;",[1825,3984,2143],{"className":3985,"style":2302},[1886,1887],[1825,3987,3991,3994],{"className":3988,"style":3990},[3989],"svg-align","width:calc(100% - 0.0556em);margin-left:0.0556em;top:-3.4306em;",[1825,3992],{"className":3993,"style":3982},[2237],[1825,3995,3997],{"style":3996},"height:0.24em;",[3998,3999,4005],"svg",{"xmlns":4000,"width":4001,"height":4002,"viewBox":4003,"preserveAspectRatio":4004},"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg","100%","0.24em","0 0 1062 239","none",[4006,4007],"path",{"d":4008},"M529 0h5l519 115c5 1 9 5 9 10 0 1-1 2-1 3l-4 22\nc-1 5-5 9-11 9h-2L532 67 19 159h-2c-5 0-9-4-11-9l-5-22c-1-6 2-12 8-13z",[1825,4010,1944],{"className":4011},[1892],[1825,4013,3498],{"className":4014},[1886,1887],[1825,4016,1951],{"className":4017},[1922],[1825,4019],{"className":4020,"style":1901},[1900],[1825,4022,3902],{"className":4023},[1905],[1825,4025],{"className":4026,"style":1901},[1900],[1825,4028,4030,4033,4036,4039,4042,4046],{"className":4029},[1877],[1825,4031],{"className":4032,"style":1882},[1881],[1825,4034,3823],{"className":4035},[1886,1887],[1825,4037,1944],{"className":4038},[1892],[1825,4040,3498],{"className":4041},[1886,1887],[1825,4043,4045],{"className":4044},[1922],")}",[1825,4047,2193],{"className":4048},[1886],[1793,4050,4051],{},"规则价值为：",[1825,4053,4055],{"className":4054},[2110],[1825,4056,4058,4121],{"className":4057},[1828],[1825,4059,4061],{"className":4060},[1832],[1834,4062,4063],{"xmlns":1836,"display":2119},[1838,4064,4065,4118],{},[1841,4066,4067,4070,4072,4074,4076,4078,4080,4082,4084,4086,4088,4090,4092,4094,4096,4098,4100,4102,4104,4106,4108,4110,4112,4114,4116],{},[1844,4068,4069],{},"V",[1848,4071,1944],{"stretchy":1850},[1844,4073,2162],{},[1848,4075,1951],{"stretchy":1850},[1848,4077,2134],{},[1844,4079,1846],{},[1848,4081,1851],{"stretchy":1850},[1844,4083,1854],{},[1848,4085,1944],{"stretchy":1850},[1844,4087,2162],{},[1848,4089,1944],{"stretchy":1850},[1844,4091,1860],{},[1848,4093,1951],{"stretchy":1850},[1848,4095,1951],{"stretchy":1850},[1848,4097,1954],{},[1844,4099,3823],{},[1848,4101,1944],{"stretchy":1850},[1844,4103,1860],{},[1848,4105,1951],{"stretchy":1850},[1844,4107,2162],{},[1848,4109,1944],{"stretchy":1850},[1844,4111,1860],{},[1848,4113,1951],{"stretchy":1850},[1848,4115,1863],{"stretchy":1850},[1844,4117,2193],{"mathvariant":2176},[1865,4119,4120],{"encoding":1867},"V(d)=E[Y(d(X))-c(X)d(X)].",[1825,4122,4124,4151,4191],{"className":4123,"ariaHidden":1873},[1872],[1825,4125,4127,4130,4133,4136,4139,4142,4145,4148],{"className":4126},[1877],[1825,4128],{"className":4129,"style":1882},[1881],[1825,4131,4069],{"className":4132,"style":1896},[1886,1887],[1825,4134,1944],{"className":4135},[1892],[1825,4137,2162],{"className":4138},[1886,1887],[1825,4140,1951],{"className":4141},[1922],[1825,4143],{"className":4144,"style":1901},[1900],[1825,4146,2134],{"className":4147},[1905],[1825,4149],{"className":4150,"style":1901},[1900],[1825,4152,4154,4157,4160,4163,4166,4169,4172,4175,4178,4182,4185,4188],{"className":4153},[1877],[1825,4155],{"className":4156,"style":1882},[1881],[1825,4158,1846],{"className":4159,"style":1888},[1886,1887],[1825,4161,1851],{"className":4162},[1892],[1825,4164,1854],{"className":4165,"style":1896},[1886,1887],[1825,4167,1944],{"className":4168},[1892],[1825,4170,2162],{"className":4171},[1886,1887],[1825,4173,1944],{"className":4174},[1892],[1825,4176,1860],{"className":4177,"style":1918},[1886,1887],[1825,4179,4181],{"className":4180},[1922],"))",[1825,4183],{"className":4184,"style":1896},[1900],[1825,4186,1954],{"className":4187},[2006],[1825,4189],{"className":4190,"style":1896},[1900],[1825,4192,4194,4197,4200,4203,4206,4209,4212,4215,4218,4222],{"className":4193},[1877],[1825,4195],{"className":4196,"style":1882},[1881],[1825,4198,3823],{"className":4199},[1886,1887],[1825,4201,1944],{"className":4202},[1892],[1825,4204,1860],{"className":4205,"style":1918},[1886,1887],[1825,4207,1951],{"className":4208},[1922],[1825,4210,2162],{"className":4211},[1886,1887],[1825,4213,1944],{"className":4214},[1892],[1825,4216,1860],{"className":4217,"style":1918},[1886,1887],[1825,4219,4221],{"className":4220},[1922],")]",[1825,4223,2193],{"className":4224},[1886],[1793,4226,4227],{},"这表明模型评估不能只看 CATE 的均方误差。研究者还要评估重叠、校准、策略价值、资源约束、公平性和样本外可迁移性。",[2064,4229,4231],{"id":4230},"_3-rct-与现实世界数据的不同风险","3. RCT 与现实世界数据的不同风险",[1793,4233,4234],{},"在随机试验中，处理分配有已知机制，但样本可能选择性强、处理遵从不完全，且稀有亚组样本少。真实世界数据规模更大，却需要更强的无混杂、测量一致和正值性假设。机器学习能灵活拟合结果与倾向得分，不能从未记录的信息中创造可交换性。",[1793,4236,4237],{},"在医学以外，这套逻辑同样适用于培训、信贷、补贴和教育：",[3389,4239,4240,4256],{},[3392,4241,4242],{},[3395,4243,4244,4247,4250,4253],{},[3398,4245,4246],{},"场景",[3398,4248,4249],{},"处理",[3398,4251,4252],{},"CATE 问题",[3398,4254,4255],{},"关键风险",[3408,4257,4258,4272,4286,4300],{},[3395,4259,4260,4263,4266,4269],{},[3413,4261,4262],{},"就业培训",[3413,4264,4265],{},"是否提供课程",[3413,4267,4268],{},"哪类求职者收入改善最大",[3413,4270,4271],{},"自主报名与地区劳动力市场",[3395,4273,4274,4277,4280,4283],{},[3413,4275,4276],{},"信贷",[3413,4278,4279],{},"是否批准贷款",[3413,4281,4282],{},"对经营存续或福利的边际影响",[3413,4284,4285],{},"历史审批造成选择性标签",[3395,4287,4288,4291,4294,4297],{},[3413,4289,4290],{},"医疗",[3413,4292,4293],{},"药物 A 或 B",[3413,4295,4296],{},"谁从 A 相对 B 获益",[3413,4298,4299],{},"重叠、毒性、试验外推",[3395,4301,4302,4305,4308,4311],{},[3413,4303,4304],{},"教育",[3413,4306,4307],{},"是否提供辅导",[3413,4309,4310],{},"哪类学生成绩提高最大",[3413,4312,4313],{},"同伴干扰、教师差异",[2064,4315,3188],{"id":4316},"_4-证据边界-1",[1793,4318,4319],{},"Feuerriegel 等的文章是方法性 Perspective，不是单个治疗的效果试验。它总结了可用工具和实践风险，不能被引用为“因果机器学习已经证明个体化治疗有效”。真实部署还需要前瞻性评价、临床或制度约束、误差成本与持续监测。",[1805,4321,4323],{"id":4322},"案例三挑出最佳项目以后估计值为什么偏高","案例三：挑出“最佳项目”以后，估计值为什么偏高",[2064,4325,4327],{"id":4326},"_1-赢家诅咒的来源","1. 赢家诅咒的来源",[1793,4329,4330,4331,4360,4361,4437],{},"假设有 ",[1825,4332,4334,4348],{"className":4333},[1828],[1825,4335,4337],{"className":4336},[1832],[1834,4338,4339],{"xmlns":1836},[1838,4340,4341,4346],{},[1841,4342,4343],{},[1844,4344,4345],{},"K",[1865,4347,4345],{"encoding":1867},[1825,4349,4351],{"className":4350,"ariaHidden":1873},[1872],[1825,4352,4354,4357],{"className":4353},[1877],[1825,4355],{"className":4356,"style":3259},[1881],[1825,4358,4345],{"className":4359,"style":2892},[1886,1887]," 个项目，真实效应为 ",[1825,4362,4364,4384],{"className":4363},[1828],[1825,4365,4367],{"className":4366},[1832],[1834,4368,4369],{"xmlns":1836},[1838,4370,4371,4381],{},[1841,4372,4373],{},[2125,4374,4375,4378],{},[1844,4376,4377],{},"θ",[1844,4379,4380],{},"k",[1865,4382,4383],{"encoding":1867},"\\theta_k",[1825,4385,4387],{"className":4386,"ariaHidden":1873},[1872],[1825,4388,4390,4393],{"className":4389},[1877],[1825,4391],{"className":4392,"style":2298},[1881],[1825,4394,4396,4400],{"className":4395},[1886],[1825,4397,4377],{"className":4398,"style":4399},[1886,1887],"margin-right:0.0278em;",[1825,4401,4403],{"className":4402},[2216],[1825,4404,4406,4429],{"className":4405},[2220,2221],[1825,4407,4409,4426],{"className":4408},[2225],[1825,4410,4413],{"className":4411,"style":4412},[2229],"height:0.3361em;",[1825,4414,4416,4419],{"style":4415},"top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;",[1825,4417],{"className":4418,"style":2238},[2237],[1825,4420,4422],{"className":4421},[2242,2243,2244,2245],[1825,4423,4380],{"className":4424,"style":4425},[1886,1887,2245],"margin-right:0.0315em;",[1825,4427,2253],{"className":4428},[2252],[1825,4430,4432],{"className":4431},[2225],[1825,4433,4435],{"className":4434,"style":2260},[2229],[1825,4436],{},"，无偏估计为：",[1825,4439,4441],{"className":4440},[2110],[1825,4442,4444,4505],{"className":4443},[1828],[1825,4445,4447],{"className":4446},[1832],[1834,4448,4449],{"xmlns":1836,"display":2119},[1838,4450,4451,4502],{},[1841,4452,4453,4463,4465,4471,4473,4480,4482,4485,4487,4489,4495,4497,4499],{},[2125,4454,4455,4461],{},[3887,4456,4457,4459],{"accent":1873},[1844,4458,4377],{},[1848,4460,3893],{"stretchy":1873},[1844,4462,4380],{},[1848,4464,2134],{},[2125,4466,4467,4469],{},[1844,4468,4377],{},[1844,4470,4380],{},[1848,4472,2140],{},[2125,4474,4475,4478],{},[1844,4476,4477],{},"ε",[1844,4479,4380],{},[1848,4481,2707],{"separator":1873},[1900,4483],{"width":4484},"2em",[1844,4486,1846],{},[1848,4488,1851],{"stretchy":1850},[2125,4490,4491,4493],{},[1844,4492,4477],{},[1844,4494,4380],{},[1848,4496,1863],{"stretchy":1850},[1848,4498,2134],{},[1946,4500,4501],{},"0.",[1865,4503,4504],{"encoding":1867},"\\widehat\\theta_k=\\theta_k+\\varepsilon_k,\n\\qquad E[\\varepsilon_k]=0.",[1825,4506,4508,4595,4650,4765],{"className":4507,"ariaHidden":1873},[1872],[1825,4509,4511,4515,4586,4589,4592],{"className":4510},[1877],[1825,4512],{"className":4513,"style":4514},[1881],"height:1.0844em;vertical-align:-0.15em;",[1825,4516,4518,4552],{"className":4517},[1886],[1825,4519,4521],{"className":4520},[1886,3965],[1825,4522,4524],{"className":4523},[2220],[1825,4525,4527],{"className":4526},[2225],[1825,4528,4531,4539],{"className":4529,"style":4530},[2229],"height:0.9344em;",[1825,4532,4533,4536],{"style":3978},[1825,4534],{"className":4535,"style":3982},[2237],[1825,4537,4377],{"className":4538,"style":4399},[1886,1887],[1825,4540,4543,4546],{"className":4541,"style":4542},[3989],"width:calc(100% - 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845},[1881],"height:0.7387em;",[1825,4847,4849,4852],{"className":4848},[1886],[1825,4850,4380],{"className":4851,"style":4425},[1886,1887],[1825,4853,4855],{"className":4854},[2216],[1825,4856,4858],{"className":4857},[2220],[1825,4859,4861],{"className":4860},[2225],[1825,4862,4864],{"className":4863,"style":4845},[2229],[1825,4865,4866,4869],{"style":2406},[1825,4867],{"className":4868,"style":2238},[2237],[1825,4870,4872],{"className":4871},[2242,2243,2244,2245],[1825,4873,4800],{"className":4874},[2006,2245],[1825,4876],{"className":4877,"style":1901},[1900],[1825,4879,2134],{"className":4880},[1905],[1825,4882],{"className":4883,"style":1901},[1900],[1825,4885,4887,4891,4900,4903,4950,4953,5022],{"className":4886},[1877],[1825,4888],{"className":4889,"style":4890},[1881],"height:1.6865em;vertical-align:-0.7521em;",[1825,4892,4895,4896],{"className":4893},[4894],"mop","ar",[1825,4897,4899],{"style":4898},"margin-right:0.0139em;","g",[1825,4901],{"className":4902,"style":4706},[1900],[1825,4904,4907],{"className":4905},[4894,4906],"op-limits",[1825,4908,4910,4941],{"className":4909},[2220,2221],[1825,4911,4913,4938],{"className":4912},[2225],[1825,4914,4916,4928],{"className":4915,"style":2616},[2229],[1825,4917,4919,4922],{"style":4918},"top:-2.3479em;margin-left:0em;",[1825,4920],{"className":4921,"style":3982},[2237],[1825,4923,4925],{"className":4924},[2242,2243,2244,2245],[1825,4926,4380],{"className":4927,"style":4425},[1886,1887,2245],[1825,4929,4930,4933],{"style":3978},[1825,4931],{"className":4932,"style":3982},[2237],[1825,4934,4935],{},[1825,4936,4816],{"className":4937},[4894],[1825,4939,2253],{"className":4940},[2252],[1825,4942,4944],{"className":4943},[2225],[1825,4945,4948],{"className":4946,"style":4947},[2229],"height:0.7521em;",[1825,4949],{},[1825,4951],{"className":4952,"style":4706},[1900],[1825,4954,4956,4988],{"className":4955},[1886],[1825,4957,4959],{"className":4958},[1886,3965],[1825,4960,4962],{"className":4961},[2220],[1825,4963,4965],{"className":4964},[2225],[1825,4966,4968,4976],{"className":4967,"style":4530},[2229],[1825,4969,4970,4973],{"style":3978},[1825,4971],{"className":4972,"style":3982},[2237],[1825,4974,4377],{"className":4975,"style":4399},[1886,1887],[1825,4977,4979,4982],{"className":4978,"style":4542},[3989],[1825,4980],{"className":4981,"style":3982},[2237],[1825,4983,4984],{"style":3996},[3998,4985,4986],{"xmlns":4000,"width":4001,"height":4002,"viewBox":4003,"preserveAspectRatio":4004},[4006,4987],{"d":4008},[1825,4989,4991],{"className":4990},[2216],[1825,4992,4994,5014],{"className":4993},[2220,2221],[1825,4995,4997,5011],{"className":4996},[2225],[1825,4998,5000],{"className":4999,"style":4412},[2229],[1825,5001,5002,5005],{"style":4415},[1825,5003],{"className":5004,"style":2238},[2237],[1825,5006,5008],{"className":5007},[2242,2243,2244,2245],[1825,5009,4380],{"className":5010,"style":4425},[1886,1887,2245],[1825,5012,2253],{"className":5013},[2252],[1825,5015,5017],{"className":5016},[2225],[1825,5018,5020],{"className":5019,"style":2260},[2229],[1825,5021],{},[1825,5023,2193],{"className":5024},[1886],[1793,5026,5027,5028,5131],{},"即使每个 ",[1825,5029,5031,5053],{"className":5030},[1828],[1825,5032,5034],{"className":5033},[1832],[1834,5035,5036],{"xmlns":1836},[1838,5037,5038,5050],{},[1841,5039,5040],{},[2125,5041,5042,5048],{},[3887,5043,5044,5046],{"accent":1873},[1844,5045,4377],{},[1848,5047,3893],{"stretchy":1873},[1844,5049,4380],{},[1865,5051,5052],{"encoding":1867},"\\widehat\\theta_k",[1825,5054,5056],{"className":5055,"ariaHidden":1873},[1872],[1825,5057,5059,5062],{"className":5058},[1877],[1825,5060],{"className":5061,"style":4514},[1881],[1825,5063,5065,5097],{"className":5064},[1886],[1825,5066,5068],{"className":5067},[1886,3965],[1825,5069,5071],{"className":5070},[2220],[1825,5072,5074],{"className":5073},[2225],[1825,5075,5077,5085],{"className":5076,"style":4530},[2229],[1825,5078,5079,5082],{"style":3978},[1825,5080],{"className":5081,"style":3982},[2237],[1825,5083,4377],{"className":5084,"style":4399},[1886,1887],[1825,5086,5088,5091],{"className":5087,"style":4542},[3989],[1825,5089],{"className":5090,"style":3982},[2237],[1825,5092,5093],{"style":3996},[3998,5094,5095],{"xmlns":4000,"width":4001,"height":4002,"viewBox":4003,"preserveAspectRatio":4004},[4006,5096],{"d":4008},[1825,5098,5100],{"className":5099},[2216],[1825,5101,5103,5123],{"className":5102},[2220,2221],[1825,5104,5106,5120],{"className":5105},[2225],[1825,5107,5109],{"className":5108,"style":4412},[2229],[1825,5110,5111,5114],{"style":4415},[1825,5112],{"className":5113,"style":2238},[2237],[1825,5115,5117],{"className":5116},[2242,2243,2244,2245],[1825,5118,4380],{"className":5119,"style":4425},[1886,1887,2245],[1825,5121,2253],{"className":5122},[2252],[1825,5124,5126],{"className":5125},[2225],[1825,5127,5129],{"className":5128,"style":2260},[2229],[1825,5130],{}," 单独无偏，入选项目更可能同时拥有较高真实效应和较幸运的正噪声，因此：",[1825,5133,5135],{"className":5134},[2110],[1825,5136,5138,5186],{"className":5137},[1828],[1825,5139,5141],{"className":5140},[1832],[1834,5142,5143],{"xmlns":1836,"display":2119},[1838,5144,5145,5183],{},[1841,5146,5147,5149,5151,5165,5167,5177,5179,5181],{},[1844,5148,1846],{},[1848,5150,1851],{"stretchy":1850},[2125,5152,5153,5159],{},[3887,5154,5155,5157],{"accent":1873},[1844,5156,4377],{},[1848,5158,3893],{"stretchy":1873},[4794,5160,5161,5163],{},[1844,5162,4380],{},[1848,5164,4800],{},[1848,5166,1954],{},[2125,5168,5169,5171],{},[1844,5170,4377],{},[4794,5172,5173,5175],{},[1844,5174,4380],{},[1848,5176,4800],{},[1848,5178,1863],{"stretchy":1850},[1848,5180,3902],{},[1946,5182,4501],{},[1865,5184,5185],{"encoding":1867},"E[\\widehat\\theta_{k^*}-\\theta_{k^*}]>0.",[1825,5187,5189,5314,5401],{"className":5188,"ariaHidden":1873},[1872],[1825,5190,5192,5196,5199,5202,5305,5308,5311],{"className":5191},[1877],[1825,5193],{"className":5194,"style":5195},[1881],"height:1.1844em;vertical-align:-0.25em;",[1825,5197,1846],{"className":5198,"style":1888},[1886,1887],[1825,5200,1851],{"className":5201},[1892],[1825,5203,5205,5237],{"className":5204},[1886],[1825,5206,5208],{"className":5207},[1886,3965],[1825,5209,5211],{"className":5210},[2220],[1825,5212,5214],{"className":5213},[2225],[1825,5215,5217,5225],{"className":5216,"style":4530},[2229],[1825,5218,5219,5222],{"style":3978},[1825,5220],{"className":5221,"style":3982},[2237],[1825,5223,4377],{"className":5224,"style":4399},[1886,1887],[1825,5226,5228,5231],{"className":5227,"style":4542},[3989],[1825,5229],{"className":5230,"style":3982},[2237],[1825,5232,5233],{"style":3996},[3998,5234,5235],{"xmlns":4000,"width":4001,"height":4002,"viewBox":4003,"preserveAspectRatio":4004},[4006,5236],{"d":4008},[1825,5238,5240],{"className":5239},[2216],[1825,5241,5243,5297],{"className":5242},[2220,2221],[1825,5244,5246,5294],{"className":5245},[2225],[1825,5247,5249],{"className":5248,"style":4412},[2229],[1825,5250,5251,5254],{"style":4415},[1825,5252],{"className":5253,"style":2238},[2237],[1825,5255,5257],{"className":5256},[2242,2243,2244,2245],[1825,5258,5260],{"className":5259},[1886,2245],[1825,5261,5263,5266],{"className":5262},[1886,2245],[1825,5264,4380],{"className":5265,"style":4425},[1886,1887,2245],[1825,5267,5269],{"className":5268},[2216],[1825,5270,5272],{"className":5271},[2220],[1825,5273,5275],{"className":5274},[2225],[1825,5276,5279],{"className":5277,"style":5278},[2229],"height:0.6183em;",[1825,5280,5282,5286],{"style":5281},"top:-2.786em;margin-right:0.0714em;",[1825,5283],{"className":5284,"style":5285},[2237],"height:2.5em;",[1825,5287,5291],{"className":5288},[2242,5289,5290,2245],"reset-size3","size1",[1825,5292,4800],{"className":5293},[2006,2245],[1825,5295,2253],{"className":5296},[2252],[1825,5298,5300],{"className":5299},[2225],[1825,5301,5303],{"className":5302,"style":2260},[2229],[1825,5304],{},[1825,5306],{"className":5307,"style":1896},[1900],[1825,5309,1954],{"className":5310},[2006],[1825,5312],{"className":5313,"style":1896},[1900],[1825,5315,5317,5320,5389,5392,5395,5398],{"className":5316},[1877],[1825,5318],{"className":5319,"style":1882},[1881],[1825,5321,5323,5326],{"className":5322},[1886],[1825,5324,4377],{"className":5325,"style":4399},[1886,1887],[1825,5327,5329],{"className":5328},[2216],[1825,5330,5332,5381],{"className":5331},[2220,2221],[1825,5333,5335,5378],{"className":5334},[2225],[1825,5336,5338],{"className":5337,"style":4412},[2229],[1825,5339,5340,5343],{"style":4415},[1825,5341],{"className":5342,"style":2238},[2237],[1825,5344,5346],{"className":5345},[2242,2243,2244,2245],[1825,5347,5349],{"className":5348},[1886,2245],[1825,5350,5352,5355],{"className":5351},[1886,2245],[1825,5353,4380],{"className":5354,"style":4425},[1886,1887,2245],[1825,5356,5358],{"className":5357},[2216],[1825,5359,5361],{"className":5360},[2220],[1825,5362,5364],{"className":5363},[2225],[1825,5365,5367],{"className":5366,"style":5278},[2229],[1825,5368,5369,5372],{"style":5281},[1825,5370],{"className":5371,"style":5285},[2237],[1825,5373,5375],{"className":5374},[2242,5289,5290,2245],[1825,5376,4800],{"className":5377},[2006,2245],[1825,5379,2253],{"className":5380},[2252],[1825,5382,5384],{"className":5383},[2225],[1825,5385,5387],{"className":5386,"style":2260},[2229],[1825,5388],{},[1825,5390,1863],{"className":5391},[1922],[1825,5393],{"className":5394,"style":1901},[1900],[1825,5396,3902],{"className":5397},[1905],[1825,5399],{"className":5400,"style":1901},[1900],[1825,5402,5404,5407],{"className":5403},[1877],[1825,5405],{"className":5406,"style":4771},[1881],[1825,5408,4501],{"className":5409},[1886],[1793,5411,5412,5413,5416],{},"这不是回归到均值的口号，而是",[1800,5414,5415],{},"用同一组噪声完成选择与估计","造成的选择后偏误。",[2064,5418,5420],{"id":5419},"_2-andrewskitagawa-与-mccloskey-的贡献","2. Andrews、Kitagawa 与 McCloskey 的贡献",[1793,5422,5423],{},"Andrews、Kitagawa 与 McCloskey（2024）研究对已选“赢家”的有效推断，构造控制中位数偏误的估计量和具有覆盖保证的置信区间，并区分：",[3190,5425,5426,5432,5438],{},[1815,5427,5428,5431],{},[1800,5429,5430],{},"条件推断","：给定选中了某个项目以后仍保证覆盖；",[1815,5433,5434,5437],{},[1800,5435,5436],{},"无条件推断","：对重复执行“估计—选择”流程的平均表现保证覆盖；",[1815,5439,5440,5443],{},[1800,5441,5442],{},"混合方法","：在保证与精度之间折中。",[1793,5445,5446],{},"论文用多地点就业培训项目和高机会社区选择说明校正可能在经济意义上改变结论，同时仍可保留有信息的推断。",[2064,5448,5450],{"id":5449},"_3-为什么留一半样本复核也有代价","3. 为什么“留一半样本复核”也有代价",[1793,5452,5453],{},"样本分割可用一半数据选项目、另一半估计其效果，从而切断选择噪声与推断噪声。但它减少选优与估计所用信息，可能选到较差项目，并扩大区间。选择后推断、层级\u002F经验贝叶斯收缩、预注册独立验证各有假设与损失函数；不存在无成本修复。",[1805,5455,5457],{"id":5456},"浏览器实验从-20-个项目中选择第一名","浏览器实验：从 20 个项目中选择第一名",[1793,5459,5460],{},"代码重复 3,000 次多地点试验。每次先生成 20 个真实项目效应，再加入估计噪声并选择观测效果最大的项目。我们比较“赢家”的观测估计与真实效应，并用独立复核样本展示回归到较合理水平。",[5462,5463],"pyodide",{"code64":5464,"layout":5465,"locale":7,"packages":5466,"title":5467},"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","vertical","numpy","Python：选择最佳项目与赢家诅咒",[5469,5470],"web-r",{"code64":5471,"layout":5465,"locale":7,"title":5472},"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","R：选择最佳项目与赢家诅咒",[2064,5474,5475],{"id":5475},"实验审计",[3190,5477,5478,5481,5484,5487],{},[1815,5479,5480],{},"把项目数从 20 改为 2、50 和 200，说明候选越多为何最大噪声通常越大。",[1815,5482,5483],{},"将标准误从 0.35 降到 0.10，区分更多项目与更精确估计的作用。",[1815,5485,5486],{},"让所有真实效应都等于 0.2，验证赢家诅咒不需要真实异质性。",[1815,5488,5489],{},"当前“独立复核”仍有噪声；重复很多次后其平均值才围绕真实效应。不要把单次复核失败自动归因于原研究错误。",[1805,5491,5492],{"id":5492},"从论文到政策的审计模板",[3389,5494,5495,5505],{},[3392,5496,5497],{},[3395,5498,5499,5502],{},[3398,5500,5501],{},"环节",[3398,5503,5504],{},"必须写清的问题",[3408,5506,5507,5515,5523,5531,5539,5547,5555,5563],{},[3395,5508,5509,5512],{},[3413,5510,5511],{},"目标",[3413,5513,5514],{},"预测风险、估计 ATE\u002FCATE，还是选择政策？",[3395,5516,5517,5520],{},[3413,5518,5519],{},"数据",[3413,5521,5522],{},"谁进入样本，处理和结果怎样测量？",[3395,5524,5525,5528],{},[3413,5526,5527],{},"识别",[3413,5529,5530],{},"反事实来自随机化、阈值、工具还是无混杂？",[3395,5532,5533,5536],{},[3413,5534,5535],{},"异质性",[3413,5537,5538],{},"亚组是否事先定义，重叠是否足够？",[3395,5540,5541,5544],{},[3413,5542,5543],{},"选择",[3413,5545,5546],{},"模型、亚组或项目是否按同一数据挑选？",[3395,5548,5549,5552],{},[3413,5550,5551],{},"推断",[3413,5553,5554],{},"区间是否考虑聚类、多重比较和选择过程？",[3395,5556,5557,5560],{},[3413,5558,5559],{},"外推",[3413,5561,5562],{},"估计对应哪类边际个体与处理版本？",[3395,5564,5565,5568],{},[3413,5566,5567],{},"决策",[3413,5569,5570],{},"成本、资源、公平性和伤害怎样进入规则？",[1805,5572,5573],{"id":5573},"分层作业",[2064,5575,5576],{"id":5576},"本科生任务",[1793,5578,5579],{},"为大学准入案例画一张 DAG，至少包括家庭收入、考试成绩、申请、录取、就读和职业结果。分别说明控制申请、控制录取后变量、只比较实际就读者可能产生什么问题。",[2064,5581,5582],{"id":5582},"研究生任务",[1793,5584,5585],{},"设计一个“为 10 个地区选择最佳培训项目”的评价方案。必须给出：目标人群、福利函数、试验分层、CATE 估计、项目选择规则、选择后置信区间、独立复核和公平性审计。解释为何最大平均处理效应不一定是预算约束下价值最高的方案。",[1805,5587,5588],{"id":5588},"一手文献与延伸阅读",[1812,5590,5591,5610,5626,5642],{},[1815,5592,5593,5594,5601,5602,5604,5605,2193],{},"Chetty, R., Deming, D. J., & Friedman, J. N. (2026). ",[5595,5596,5600],"a",{"href":5597,"rel":5598},"https:\u002F\u002Facademic.oup.com\u002Fqje\u002Farticle-abstract\u002F141\u002F1\u002F51\u002F8306880",[5599],"nofollow","Diversifying Society’s Leaders? The Determinants and Causal Effects of Admission to Highly Selective Private Colleges",". ",[2072,5603,2074],{},", 141(1), 51–145. DOI: ",[5595,5606,5609],{"href":5607,"rel":5608},"https:\u002F\u002Fdoi.org\u002F10.1093\u002Fqje\u002Fqjaf050",[5599],"10.1093\u002Fqje\u002Fqjaf050",[1815,5611,5612,5613,5601,5618,5620,5621,2193],{},"Mountjoy, J. (2026). ",[5595,5614,5617],{"href":5615,"rel":5616},"https:\u002F\u002Facademic.oup.com\u002Fqje\u002Farticle\u002F141\u002F1\u002F429\u002F8376650",[5599],"Marginal Returns to Public Universities",[2072,5619,2074],{},", 141(1), 429–497. DOI: ",[5595,5622,5625],{"href":5623,"rel":5624},"https:\u002F\u002Fdoi.org\u002F10.1093\u002Fqje\u002Fqjaf055",[5599],"10.1093\u002Fqje\u002Fqjaf055",[1815,5627,5628,5629,5601,5634,5636,5637,2193],{},"Feuerriegel, S. et al. (2024). ",[5595,5630,5633],{"href":5631,"rel":5632},"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-024-02902-1",[5599],"Causal Machine Learning for Predicting Treatment Outcomes",[2072,5635,3471],{},", 30, 958–968. DOI: ",[5595,5638,5641],{"href":5639,"rel":5640},"https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41591-024-02902-1",[5599],"10.1038\u002Fs41591-024-02902-1",[1815,5643,5644,5645,5601,5650,5652,5653,2193],{},"Andrews, I., Kitagawa, T., & McCloskey, A. (2024). ",[5595,5646,5649],{"href":5647,"rel":5648},"https:\u002F\u002Facademic.oup.com\u002Fqje\u002Farticle\u002F139\u002F1\u002F305\u002F7276491",[5599],"Inference on Winners",[2072,5651,2074],{},", 139(1), 305–358. DOI: ",[5595,5654,5657],{"href":5655,"rel":5656},"https:\u002F\u002Fdoi.org\u002F10.1093\u002Fqje\u002Fqjad043",[5599],"10.1093\u002Fqje\u002Fqjad043",[1793,5659,5660,5661,5665,5666,5670,5671,5675],{},"建议先复习",[5595,5662,5664],{"href":5663},".\u002F03-iv\u002F","工具变量","、",[5595,5667,5669],{"href":5668},".\u002F05-did\u002F","双重差分","和",[5595,5672,5674],{"href":5673},".\u002F11-ml-causal\u002F","机器学习与因果推断","，再完成本章研究设计审计。",{"title":10,"searchDepth":5677,"depth":5677,"links":5678},2,[5679,5680,5689,5695,5700,5703,5704,5708],{"id":1807,"depth":5677,"text":1807},{"id":2061,"depth":5677,"text":2062,"children":5681},[5682,5684,5685,5686,5687,5688],{"id":2066,"depth":5683,"text":2067},3,{"id":2102,"depth":5683,"text":2103},{"id":3173,"depth":5683,"text":3174},{"id":3187,"depth":5683,"text":3188},{"id":3209,"depth":5683,"text":3210},{"id":3373,"depth":5683,"text":3374},{"id":3460,"depth":5677,"text":3461,"children":5690},[5691,5692,5693,5694],{"id":3464,"depth":5683,"text":3465},{"id":3804,"depth":5683,"text":3805},{"id":4230,"depth":5683,"text":4231},{"id":4316,"depth":5683,"text":3188},{"id":4322,"depth":5677,"text":4323,"children":5696},[5697,5698,5699],{"id":4326,"depth":5683,"text":4327},{"id":5419,"depth":5683,"text":5420},{"id":5449,"depth":5683,"text":5450},{"id":5456,"depth":5677,"text":5457,"children":5701},[5702],{"id":5475,"depth":5683,"text":5475},{"id":5492,"depth":5677,"text":5492},{"id":5573,"depth":5677,"text":5573,"children":5705},[5706,5707],{"id":5576,"depth":5683,"text":5576},{"id":5582,"depth":5683,"text":5582},{"id":5588,"depth":5677,"text":5588},"用高校准入、因果机器学习与选择后推断案例训练现代微观计量的识别、异质性和政策决策能力。","md",{"sidebar":5712},{"order":5713},12,true,{"title":1540,"description":5709},"d_y45CXpdOxfkEpDQ13GeS_JbUAc2jtzK4dwYbvpXaA",[5718,5720],{"title":1534,"path":1535,"stem":1536,"description":5719,"children":-1},"用正交化与交叉拟合连接高维预测、平均效应、异质性效应和政策学习。",{"title":1548,"path":1549,"stem":1550,"description":5721,"children":-1},"高级微观经济学笔记，覆盖消费者理论、福利分析、不确定性、博弈、信息经济学和市场设计。",1785754752392]