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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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Alpha 策略全流程","\u002Fzh\u002Fquant\u002F09-case-study","zh\u002Fquant\u002F09-case-study",{"title":1779,"path":1780,"stem":1781},"量化投资文献与软件图谱","\u002Fzh\u002Fquant\u002F10-reading-software-map","zh\u002Fquant\u002F10-reading-software-map",null,{"id":1784,"title":1534,"body":1785,"description":4469,"extension":4470,"features":1782,"hero":1782,"layout":1782,"locale":1782,"meta":4471,"navigation":1782,"path":1535,"published":4474,"seo":4475,"stem":1536,"__hash__":4476},"docs\u002Fzh\u002Fmicroeconometrics\u002F11-ml-causal\u002Findex.md",{"type":1786,"value":1787,"toc":4455},"minimark",[1788,1792,1803,2001,2005,2008,2027,2031,2527,2530,2534,2537,2854,2857,3064,3067,3400,3593,3608,3612,3615,3622,3625,3629,3816,3819,3833,3836,3840,3888,4066,4069,4240,4243,4247,4250,4267,4270,4274,4336,4352,4356,4382,4385,4388,4405,4408,4443],[1789,1790,1534],"h1",{"id":1791},"第十一章机器学习与因果推断",[1793,1794,1795],"blockquote",{},[1796,1797,1798,1802],"p",{},[1799,1800,1801],"strong",{},"案例："," 一个模型能准确预测谁会失业，是否也能告诉我们培训应优先给谁？",[1796,1804,1805,1806,1905,1906,1954,1955,2000],{},"预测模型学习 ",[1807,1808,1811,1851],"span",{"className":1809},[1810],"katex",[1807,1812,1815],{"className":1813},[1814],"katex-mathml",[1816,1817,1819],"math",{"xmlns":1818},"http:\u002F\u002Fwww.w3.org\u002F1998\u002FMath\u002FMathML",[1820,1821,1822,1846],"semantics",{},[1823,1824,1825,1829,1834,1837,1840,1843],"mrow",{},[1826,1827,1828],"mi",{},"E",[1830,1831,1833],"mo",{"stretchy":1832},"false","[",[1826,1835,1836],{},"Y",[1830,1838,1839],{},"∣",[1826,1841,1842],{},"X",[1830,1844,1845],{"stretchy":1832},"]",[1847,1848,1850],"annotation",{"encoding":1849},"application\u002Fx-tex","E[Y\\mid X]",[1807,1852,1856,1891],{"className":1853,"ariaHidden":1855},[1854],"katex-html","true",[1807,1857,1860,1865,1871,1875,1879,1884,1888],{"className":1858},[1859],"base",[1807,1861],{"className":1862,"style":1864},[1863],"strut","height:1em;vertical-align:-0.25em;",[1807,1866,1828],{"className":1867,"style":1870},[1868,1869],"mord","mathnormal","margin-right:0.0576em;",[1807,1872,1833],{"className":1873},[1874],"mopen",[1807,1876,1836],{"className":1877,"style":1878},[1868,1869],"margin-right:0.2222em;",[1807,1880],{"className":1881,"style":1883},[1882],"mspace","margin-right:0.2778em;",[1807,1885,1839],{"className":1886},[1887],"mrel",[1807,1889],{"className":1890,"style":1883},[1882],[1807,1892,1894,1897,1901],{"className":1893},[1859],[1807,1895],{"className":1896,"style":1864},[1863],[1807,1898,1842],{"className":1899,"style":1900},[1868,1869],"margin-right:0.0785em;",[1807,1902,1845],{"className":1903},[1904],"mclose","；因果决策需要比较 ",[1807,1907,1909,1933],{"className":1908},[1810],[1807,1910,1912],{"className":1911},[1814],[1816,1913,1914],{"xmlns":1818},[1820,1915,1916,1930],{},[1823,1917,1918,1920,1923,1927],{},[1826,1919,1836],{},[1830,1921,1922],{"stretchy":1832},"(",[1924,1925,1926],"mn",{},"1",[1830,1928,1929],{"stretchy":1832},")",[1847,1931,1932],{"encoding":1849},"Y(1)",[1807,1934,1936],{"className":1935,"ariaHidden":1855},[1854],[1807,1937,1939,1942,1945,1948,1951],{"className":1938},[1859],[1807,1940],{"className":1941,"style":1864},[1863],[1807,1943,1836],{"className":1944,"style":1878},[1868,1869],[1807,1946,1922],{"className":1947},[1874],[1807,1949,1926],{"className":1950},[1868],[1807,1952,1929],{"className":1953},[1904]," 与 ",[1807,1956,1958,1979],{"className":1957},[1810],[1807,1959,1961],{"className":1960},[1814],[1816,1962,1963],{"xmlns":1818},[1820,1964,1965,1976],{},[1823,1966,1967,1969,1971,1974],{},[1826,1968,1836],{},[1830,1970,1922],{"stretchy":1832},[1924,1972,1973],{},"0",[1830,1975,1929],{"stretchy":1832},[1847,1977,1978],{"encoding":1849},"Y(0)",[1807,1980,1982],{"className":1981,"ariaHidden":1855},[1854],[1807,1983,1985,1988,1991,1994,1997],{"className":1984},[1859],[1807,1986],{"className":1987,"style":1864},[1863],[1807,1989,1836],{"className":1990,"style":1878},[1868,1869],[1807,1992,1922],{"className":1993},[1874],[1807,1995,1973],{"className":1996},[1868],[1807,1998,1929],{"className":1999},[1904],"。机器学习可灵活估计高维 nuisance functions，但不能创造随机化、无混杂或重叠。",[2002,2003,2004],"h2",{"id":2004},"学习目标",[1796,2006,2007],{},"你应能：",[2009,2010,2011,2015,2018,2021,2024],"ol",{},[2012,2013,2014],"li",{},"区分风险预测、ATE、CATE 与政策价值；",[2012,2016,2017],{},"解释正交化为何降低 nuisance model 误差的一阶影响；",[2012,2019,2020],{},"实施样本分割与交叉拟合；",[2012,2022,2023],{},"识别因果森林、DML 和政策学习的不同目标；",[2012,2025,2026],{},"审查重叠、目标泄漏、选择后推断与公平性。",[2002,2028,2030],{"id":2029},"_1-预测问题不等于因果问题","1. 预测问题不等于因果问题",[2032,2033,2034,2050],"table",{},[2035,2036,2037],"thead",{},[2038,2039,2040,2044,2047],"tr",{},[2041,2042,2043],"th",{},"任务",[2041,2045,2046],{},"目标",[2041,2048,2049],{},"例子",[2051,2052,2053,2129,2235,2388],"tbody",{},[2038,2054,2055,2059,2126],{},[2056,2057,2058],"td",{},"风险预测",[2056,2060,2061],{},[1807,2062,2064,2087],{"className":2063},[1810],[1807,2065,2067],{"className":2066},[1814],[1816,2068,2069],{"xmlns":1818},[1820,2070,2071,2085],{},[1823,2072,2073,2075,2077,2079,2081,2083],{},[1826,2074,1828],{},[1830,2076,1833],{"stretchy":1832},[1826,2078,1836],{},[1830,2080,1839],{},[1826,2082,1842],{},[1830,2084,1845],{"stretchy":1832},[1847,2086,1850],{"encoding":1849},[1807,2088,2090,2114],{"className":2089,"ariaHidden":1855},[1854],[1807,2091,2093,2096,2099,2102,2105,2108,2111],{"className":2092},[1859],[1807,2094],{"className":2095,"style":1864},[1863],[1807,2097,1828],{"className":2098,"style":1870},[1868,1869],[1807,2100,1833],{"className":2101},[1874],[1807,2103,1836],{"className":2104,"style":1878},[1868,1869],[1807,2106],{"className":2107,"style":1883},[1882],[1807,2109,1839],{"className":2110},[1887],[1807,2112],{"className":2113,"style":1883},[1882],[1807,2115,2117,2120,2123],{"className":2116},[1859],[1807,2118],{"className":2119,"style":1864},[1863],[1807,2121,1842],{"className":2122,"style":1900},[1868,1869],[1807,2124,1845],{"className":2125},[1904],[2056,2127,2128],{},"谁最可能失业？",[2038,2130,2131,2134,2232],{},[2056,2132,2133],{},"平均效应",[2056,2135,2136],{},[1807,2137,2139,2176],{"className":2138},[1810],[1807,2140,2142],{"className":2141},[1814],[1816,2143,2144],{"xmlns":1818},[1820,2145,2146,2173],{},[1823,2147,2148,2150,2152,2154,2156,2158,2160,2163,2165,2167,2169,2171],{},[1826,2149,1828],{},[1830,2151,1833],{"stretchy":1832},[1826,2153,1836],{},[1830,2155,1922],{"stretchy":1832},[1924,2157,1926],{},[1830,2159,1929],{"stretchy":1832},[1830,2161,2162],{},"−",[1826,2164,1836],{},[1830,2166,1922],{"stretchy":1832},[1924,2168,1973],{},[1830,2170,1929],{"stretchy":1832},[1830,2172,1845],{"stretchy":1832},[1847,2174,2175],{"encoding":1849},"E[Y(1)-Y(0)]",[1807,2177,2179,2213],{"className":2178,"ariaHidden":1855},[1854],[1807,2180,2182,2185,2188,2191,2194,2197,2200,2203,2206,2210],{"className":2181},[1859],[1807,2183],{"className":2184,"style":1864},[1863],[1807,2186,1828],{"className":2187,"style":1870},[1868,1869],[1807,2189,1833],{"className":2190},[1874],[1807,2192,1836],{"className":2193,"style":1878},[1868,1869],[1807,2195,1922],{"className":2196},[1874],[1807,2198,1926],{"className":2199},[1868],[1807,2201,1929],{"className":2202},[1904],[1807,2204],{"className":2205,"style":1878},[1882],[1807,2207,2162],{"className":2208},[2209],"mbin",[1807,2211],{"className":2212,"style":1878},[1882],[1807,2214,2216,2219,2222,2225,2228],{"className":2215},[1859],[1807,2217],{"className":2218,"style":1864},[1863],[1807,2220,1836],{"className":2221,"style":1878},[1868,1869],[1807,2223,1922],{"className":2224},[1874],[1807,2226,1973],{"className":2227},[1868],[1807,2229,2231],{"className":2230},[1904],")]",[2056,2233,2234],{},"培训平均提高多少收入？",[2038,2236,2237,2240,2385],{},[2056,2238,2239],{},"条件效应",[2056,2241,2242],{},[1807,2243,2245,2291],{"className":2244},[1810],[1807,2246,2248],{"className":2247},[1814],[1816,2249,2250],{"xmlns":1818},[1820,2251,2252,2288],{},[1823,2253,2254,2256,2258,2260,2262,2264,2266,2268,2270,2272,2274,2276,2278,2280,2283,2286],{},[1826,2255,1828],{},[1830,2257,1833],{"stretchy":1832},[1826,2259,1836],{},[1830,2261,1922],{"stretchy":1832},[1924,2263,1926],{},[1830,2265,1929],{"stretchy":1832},[1830,2267,2162],{},[1826,2269,1836],{},[1830,2271,1922],{"stretchy":1832},[1924,2273,1973],{},[1830,2275,1929],{"stretchy":1832},[1830,2277,1839],{},[1826,2279,1842],{},[1830,2281,2282],{},"=",[1826,2284,2285],{},"x",[1830,2287,1845],{"stretchy":1832},[1847,2289,2290],{"encoding":1849},"E[Y(1)-Y(0)\\mid X=x]",[1807,2292,2294,2327,2354,2373],{"className":2293,"ariaHidden":1855},[1854],[1807,2295,2297,2300,2303,2306,2309,2312,2315,2318,2321,2324],{"className":2296},[1859],[1807,2298],{"className":2299,"style":1864},[1863],[1807,2301,1828],{"className":2302,"style":1870},[1868,1869],[1807,2304,1833],{"className":2305},[1874],[1807,2307,1836],{"className":2308,"style":1878},[1868,1869],[1807,2310,1922],{"className":2311},[1874],[1807,2313,1926],{"className":2314},[1868],[1807,2316,1929],{"className":2317},[1904],[1807,2319],{"className":2320,"style":1878},[1882],[1807,2322,2162],{"className":2323},[2209],[1807,2325],{"className":2326,"style":1878},[1882],[1807,2328,2330,2333,2336,2339,2342,2345,2348,2351],{"className":2329},[1859],[1807,2331],{"className":2332,"style":1864},[1863],[1807,2334,1836],{"className":2335,"style":1878},[1868,1869],[1807,2337,1922],{"className":2338},[1874],[1807,2340,1973],{"className":2341},[1868],[1807,2343,1929],{"className":2344},[1904],[1807,2346],{"className":2347,"style":1883},[1882],[1807,2349,1839],{"className":2350},[1887],[1807,2352],{"className":2353,"style":1883},[1882],[1807,2355,2357,2361,2364,2367,2370],{"className":2356},[1859],[1807,2358],{"className":2359,"style":2360},[1863],"height:0.6833em;",[1807,2362,1842],{"className":2363,"style":1900},[1868,1869],[1807,2365],{"className":2366,"style":1883},[1882],[1807,2368,2282],{"className":2369},[1887],[1807,2371],{"className":2372,"style":1883},[1882],[1807,2374,2376,2379,2382],{"className":2375},[1859],[1807,2377],{"className":2378,"style":1864},[1863],[1807,2380,2285],{"className":2381},[1868,1869],[1807,2383,1845],{"className":2384},[1904],[2056,2386,2387],{},"哪类人因培训受益更多？",[2038,2389,2390,2393,2524],{},[2056,2391,2392],{},"政策学习",[2056,2394,2395,2396],{},"最大化 ",[1807,2397,2399,2451],{"className":2398},[1810],[1807,2400,2402],{"className":2401},[1814],[1816,2403,2404],{"xmlns":1818},[1820,2405,2406,2448],{},[1823,2407,2408,2410,2412,2414,2416,2419,2421,2423,2425,2427,2429,2432,2434,2436,2438,2440,2442,2444,2446],{},[1826,2409,1828],{},[1830,2411,1833],{"stretchy":1832},[1826,2413,1836],{},[1830,2415,1922],{"stretchy":1832},[1826,2417,2418],{},"d",[1830,2420,1922],{"stretchy":1832},[1826,2422,1842],{},[1830,2424,1929],{"stretchy":1832},[1830,2426,1929],{"stretchy":1832},[1830,2428,2162],{},[1826,2430,2431],{},"c",[1830,2433,1922],{"stretchy":1832},[1826,2435,1842],{},[1830,2437,1929],{"stretchy":1832},[1826,2439,2418],{},[1830,2441,1922],{"stretchy":1832},[1826,2443,1842],{},[1830,2445,1929],{"stretchy":1832},[1830,2447,1845],{"stretchy":1832},[1847,2449,2450],{"encoding":1849},"E[Y(d(X))-c(X)d(X)]",[1807,2452,2454,2494],{"className":2453,"ariaHidden":1855},[1854],[1807,2455,2457,2460,2463,2466,2469,2472,2475,2478,2481,2485,2488,2491],{"className":2456},[1859],[1807,2458],{"className":2459,"style":1864},[1863],[1807,2461,1828],{"className":2462,"style":1870},[1868,1869],[1807,2464,1833],{"className":2465},[1874],[1807,2467,1836],{"className":2468,"style":1878},[1868,1869],[1807,2470,1922],{"className":2471},[1874],[1807,2473,2418],{"className":2474},[1868,1869],[1807,2476,1922],{"className":2477},[1874],[1807,2479,1842],{"className":2480,"style":1900},[1868,1869],[1807,2482,2484],{"className":2483},[1904],"))",[1807,2486],{"className":2487,"style":1878},[1882],[1807,2489,2162],{"className":2490},[2209],[1807,2492],{"className":2493,"style":1878},[1882],[1807,2495,2497,2500,2503,2506,2509,2512,2515,2518,2521],{"className":2496},[1859],[1807,2498],{"className":2499,"style":1864},[1863],[1807,2501,2431],{"className":2502},[1868,1869],[1807,2504,1922],{"className":2505},[1874],[1807,2507,1842],{"className":2508,"style":1900},[1868,1869],[1807,2510,1929],{"className":2511},[1904],[1807,2513,2418],{"className":2514},[1868,1869],[1807,2516,1922],{"className":2517},[1874],[1807,2519,1842],{"className":2520,"style":1900},[1868,1869],[1807,2522,2231],{"className":2523},[1904],[2056,2525,2526],{},"名额有限时培训给谁？",[1796,2528,2529],{},"高风险者不一定最受益。例如长期失业者的基准风险高，但课程可能只对具备某些数字技能者有效。",[2002,2531,2533],{"id":2532},"_2-double-debiased-machine-learning","2. Double \u002F Debiased Machine Learning",[1796,2535,2536],{},"部分线性模型：",[1807,2538,2541],{"className":2539},[2540],"katex-display",[1807,2542,2544,2644],{"className":2543},[1810],[1807,2545,2547],{"className":2546},[1814],[1816,2548,2550],{"xmlns":1818,"display":2549},"block",[1820,2551,2552,2641],{},[2553,2554,2558,2605],"mtable",{"rowspacing":2555,"columnalign":2556,"columnspacing":2557},"0.25em","right left","0em",[2559,2560,2561,2569],"mtr",{},[2562,2563,2564],"mtd",{},[2565,2566,2567],"mstyle",{"scriptlevel":1973,"displaystyle":1855},[1826,2568,1836],{},[2562,2570,2571],{},[2565,2572,2573],{"scriptlevel":1973,"displaystyle":1855},[1823,2574,2575,2577,2579,2582,2585,2588,2591,2593,2595,2597,2599,2602],{},[1823,2576],{},[1830,2578,2282],{},[1826,2580,2581],{},"θ",[1826,2583,2584],{},"D",[1830,2586,2587],{},"+",[1826,2589,2590],{},"g",[1830,2592,1922],{"stretchy":1832},[1826,2594,1842],{},[1830,2596,1929],{"stretchy":1832},[1830,2598,2587],{},[1826,2600,2601],{},"ε",[1830,2603,2604],{"separator":1855},",",[2559,2606,2607,2613],{},[2562,2608,2609],{},[2565,2610,2611],{"scriptlevel":1973,"displaystyle":1855},[1826,2612,2584],{},[2562,2614,2615],{},[2565,2616,2617],{"scriptlevel":1973,"displaystyle":1855},[1823,2618,2619,2621,2623,2626,2628,2630,2632,2634,2637],{},[1823,2620],{},[1830,2622,2282],{},[1826,2624,2625],{},"m",[1830,2627,1922],{"stretchy":1832},[1826,2629,1842],{},[1830,2631,1929],{"stretchy":1832},[1830,2633,2587],{},[1826,2635,2636],{},"v",[1826,2638,2640],{"mathvariant":2639},"normal",".",[1847,2642,2643],{"encoding":1849},"\\begin{aligned}\nY &= \\theta D+g(X)+\\varepsilon,\\\\\nD &= 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",[1807,3467,3469,3487],{"className":3468},[1810],[1807,3470,3472],{"className":3471},[1814],[1816,3473,3474],{"xmlns":1818},[1820,3475,3476,3484],{},[1823,3477,3478],{},[3083,3479,3480,3482],{"accent":1855},[1826,3481,2584],{},[1830,3483,3089],{"stretchy":1855},[1847,3485,3486],{"encoding":1849},"\\widetilde D",[1807,3488,3490],{"className":3489,"ariaHidden":1855},[1854],[1807,3491,3493,3496],{"className":3492},[1859],[1807,3494],{"className":3495,"style":3151},[1863],[1807,3497,3499],{"className":3498},[1868,3155],[1807,3500,3502],{"className":3501},[2667],[1807,3503,3505],{"className":3504},[2672],[1807,3506,3508,3516],{"className":3507,"style":3151},[2676],[1807,3509,3510,3513],{"style":3167},[1807,3511],{"className":3512,"style":2685},[2684],[1807,3514,2584],{"className":3515,"style":2704},[1868,1869],[1807,3517,3519,3522],{"className":3518,"style":3311},[3177],[1807,3520],{"className":3521,"style":2685},[2684],[1807,3523,3524],{"style":3184},[3186,3525,3526],{"xmlns":3188,"width":3189,"height":3190,"viewBox":3191,"preserveAspectRatio":3192},[3194,3527],{"d":3196},"。正交矩条件使小幅 nuisance 误差对 ",[1807,3530,3532,3550],{"className":3531},[1810],[1807,3533,3535],{"className":3534},[1814],[1816,3536,3537],{"xmlns":1818},[1820,3538,3539,3547],{},[1823,3540,3541],{},[3083,3542,3543,3545],{"accent":1855},[1826,3544,2581],{},[1830,3546,3102],{"stretchy":1855},[1847,3548,3549],{"encoding":1849},"\\widehat\\theta",[1807,3551,3553],{"className":3552,"ariaHidden":1855},[1854],[1807,3554,3556,3559],{"className":3555},[1859],[1807,3557],{"className":3558,"style":3244},[1863],[1807,3560,3562],{"className":3561},[1868,3155],[1807,3563,3565],{"className":3564},[2667],[1807,3566,3568],{"className":3567},[2672],[1807,3569,3571,3579],{"className":3570,"style":3244},[2676],[1807,3572,3573,3576],{"style":3167},[1807,3574],{"className":3575,"style":2685},[2684],[1807,3577,2581],{"className":3578,"style":2704},[1868,1869],[1807,3580,3583,3586],{"className":3581,"style":3582},[3177],"width:calc(100% - 0.1667em);margin-left:0.1667em;top:-3.6944em;",[1807,3584],{"className":3585,"style":2685},[2684],[1807,3587,3588],{"style":3262},[3186,3589,3590],{"xmlns":3188,"width":3189,"height":3265,"viewBox":3266,"preserveAspectRatio":3192},[3194,3591],{"d":3269}," 的一阶影响为零，但仍要求：",[3594,3595,3596,3599,3602,3605],"ul",{},[2012,3597,3598],{},"识别假设成立；",[2012,3600,3601],{},"nuisance 估计足够好；",[2012,3603,3604],{},"有重叠；",[2012,3606,3607],{},"使用交叉拟合避免同一观测既训练又残差化。",[2002,3609,3611],{"id":3610},"_3-可运行案例非线性选择下的交叉拟合-dml","3. 可运行案例：非线性选择下的交叉拟合 DML",[1796,3613,3614],{},"培训参加概率和基准收入都以非线性方式依赖特征。朴素均值差有混淆；两折交叉拟合在未参与训练的观测上预测 nuisance functions，再用残差识别处理效应。",[3616,3617],"pyodide",{"code64":3618,"layout":3619,"locale":7,"packages":3620,"title":3621},"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","vertical","numpy","Python：两折交叉拟合 DML",[1796,3623,3624],{},"此例知道正确的特征基函数，目的只是展示正交化流程。现实研究应把 learner、调参、折数、随机种子和预处理全部纳入可复现管线，并使用 DML 的有效方差公式。",[2002,3626,3628],{"id":3627},"_4-cate-与因果森林","4. CATE 与因果森林",[1807,3630,3632],{"className":3631},[2540],[1807,3633,3635,3692],{"className":3634},[1810],[1807,3636,3638],{"className":3637},[1814],[1816,3639,3640],{"xmlns":1818,"display":2549},[1820,3641,3642,3689],{},[1823,3643,3644,3647,3649,3651,3653,3655,3657,3659,3661,3663,3665,3667,3669,3671,3673,3675,3677,3679,3681,3683,3685,3687],{},[1826,3645,3646],{},"τ",[1830,3648,1922],{"stretchy":1832},[1826,3650,2285],{},[1830,3652,1929],{"stretchy":1832},[1830,3654,2282],{},[1826,3656,1828],{},[1830,3658,1833],{"stretchy":1832},[1826,3660,1836],{},[1830,3662,1922],{"stretchy":1832},[1924,3664,1926],{},[1830,3666,1929],{"stretchy":1832},[1830,3668,2162],{},[1826,3670,1836],{},[1830,3672,1922],{"stretchy":1832},[1924,3674,1973],{},[1830,3676,1929],{"stretchy":1832},[1830,3678,1839],{},[1826,3680,1842],{},[1830,3682,2282],{},[1826,3684,2285],{},[1830,3686,1845],{"stretchy":1832},[1826,3688,2640],{"mathvariant":2639},[1847,3690,3691],{"encoding":1849},"\\tau(x)=E[Y(1)-Y(0)\\mid X=x].",[1807,3693,3695,3723,3756,3783,3801],{"className":3694,"ariaHidden":1855},[1854],[1807,3696,3698,3701,3705,3708,3711,3714,3717,3720],{"className":3697},[1859],[1807,3699],{"className":3700,"style":1864},[1863],[1807,3702,3646],{"className":3703,"style":3704},[1868,1869],"margin-right:0.1132em;",[1807,3706,1922],{"className":3707},[1874],[1807,3709,2285],{"className":3710},[1868,1869],[1807,3712,1929],{"className":3713},[1904],[1807,3715],{"className":3716,"style":1883},[1882],[1807,3718,2282],{"className":3719},[1887],[1807,3721],{"className":3722,"style":1883},[1882],[1807,3724,3726,3729,3732,3735,3738,3741,3744,3747,3750,3753],{"className":3725},[1859],[1807,3727],{"className":3728,"style":1864},[1863],[1807,3730,1828],{"className":3731,"style":1870},[1868,1869],[1807,3733,1833],{"className":3734},[1874],[1807,3736,1836],{"className":3737,"style":1878},[1868,1869],[1807,3739,1922],{"className":3740},[1874],[1807,3742,1926],{"className":3743},[1868],[1807,3745,1929],{"className":3746},[1904],[1807,3748],{"className":3749,"style":1878},[1882],[1807,3751,2162],{"className":3752},[2209],[1807,3754],{"className":3755,"style":1878},[1882],[1807,3757,3759,3762,3765,3768,3771,3774,3777,3780],{"className":3758},[1859],[1807,3760],{"className":3761,"style":1864},[1863],[1807,3763,1836],{"className":3764,"style":1878},[1868,1869],[1807,3766,1922],{"className":3767},[1874],[1807,3769,1973],{"className":3770},[1868],[1807,3772,1929],{"className":3773},[1904],[1807,3775],{"className":3776,"style":1883},[1882],[1807,3778,1839],{"className":3779},[1887],[1807,3781],{"className":3782,"style":1883},[1882],[1807,3784,3786,3789,3792,3795,3798],{"className":3785},[1859],[1807,3787],{"className":3788,"style":2360},[1863],[1807,3790,1842],{"className":3791,"style":1900},[1868,1869],[1807,3793],{"className":3794,"style":1883},[1882],[1807,3796,2282],{"className":3797},[1887],[1807,3799],{"className":3800,"style":1883},[1882],[1807,3802,3804,3807,3810,3813],{"className":3803},[1859],[1807,3805],{"className":3806,"style":1864},[1863],[1807,3808,2285],{"className":3809},[1868,1869],[1807,3811,1845],{"className":3812},[1904],[1807,3814,2640],{"className":3815},[1868],[1796,3817,3818],{},"因果森林通过诚实样本分割、局部加权和正交化估计异质性。应避免：",[3594,3820,3821,3824,3827,3830],{},[2012,3822,3823],{},"看完同一数据后挑“最显著亚组”；",[2012,3825,3826],{},"只展示变量重要性，未报告 CATE 校准；",[2012,3828,3829],{},"在重叠很差区域解释个体效应；",[2012,3831,3832],{},"把噪声很大的个体点估计用于高风险决策。",[1796,3834,3835],{},"更稳健的报告通常按预先定义或样本外分组展示平均效应，并给出组间差异的有效推断。",[2002,3837,3839],{"id":3838},"_5-从-cate-到政策规则","5. 从 CATE 到政策规则",[1796,3841,3842,3843,3887],{},"若处理成本为 ",[1807,3844,3846,3866],{"className":3845},[1810],[1807,3847,3849],{"className":3848},[1814],[1816,3850,3851],{"xmlns":1818},[1820,3852,3853,3863],{},[1823,3854,3855,3857,3859,3861],{},[1826,3856,2431],{},[1830,3858,1922],{"stretchy":1832},[1826,3860,2285],{},[1830,3862,1929],{"stretchy":1832},[1847,3864,3865],{"encoding":1849},"c(x)",[1807,3867,3869],{"className":3868,"ariaHidden":1855},[1854],[1807,3870,3872,3875,3878,3881,3884],{"className":3871},[1859],[1807,3873],{"className":3874,"style":1864},[1863],[1807,3876,2431],{"className":3877},[1868,1869],[1807,3879,1922],{"className":3880},[1874],[1807,3882,2285],{"className":3883},[1868,1869],[1807,3885,1929],{"className":3886},[1904],"，简单规则：",[1807,3889,3891],{"className":3890},[2540],[1807,3892,3894,3950],{"className":3893},[1810],[1807,3895,3897],{"className":3896},[1814],[1816,3898,3899],{"xmlns":1818,"display":2549},[1820,3900,3901,3947],{},[1823,3902,3903,3905,3907,3909,3911,3913,3916,3919,3925,3927,3929,3931,3934,3936,3938,3940,3942,3945],{},[1826,3904,2418],{},[1830,3906,1922],{"stretchy":1832},[1826,3908,2285],{},[1830,3910,1929],{"stretchy":1832},[1830,3912,2282],{},[1924,3914,1926],{"mathvariant":3915},"bold",[1830,3917,3918],{"stretchy":1832},"{",[3083,3920,3921,3923],{"accent":1855},[1826,3922,3646],{},[1830,3924,3102],{"stretchy":1855},[1830,3926,1922],{"stretchy":1832},[1826,3928,2285],{},[1830,3930,1929],{"stretchy":1832},[1830,3932,3933],{},">",[1826,3935,2431],{},[1830,3937,1922],{"stretchy":1832},[1826,3939,2285],{},[1830,3941,1929],{"stretchy":1832},[1830,3943,3944],{"stretchy":1832},"}",[1826,3946,2640],{"mathvariant":2639},[1847,3948,3949],{"encoding":1849},"d(x)=\\mathbf 1\\{\\widehat\\tau(x)>c(x)\\}.",[1807,3951,3953,3980,4044],{"className":3952,"ariaHidden":1855},[1854],[1807,3954,3956,3959,3962,3965,3968,3971,3974,3977],{"className":3955},[1859],[1807,3957],{"className":3958,"style":1864},[1863],[1807,3960,2418],{"className":3961},[1868,1869],[1807,3963,1922],{"className":3964},[1874],[1807,3966,2285],{"className":3967},[1868,1869],[1807,3969,1929],{"className":3970},[1904],[1807,3972],{"className":3973,"style":1883},[1882],[1807,3975,2282],{"className":3976},[1887],[1807,3978],{"className":3979,"style":1883},[1882],[1807,3981,3983,3986,3990,3993,4026,4029,4032,4035,4038,4041],{"className":3982},[1859],[1807,3984],{"className":3985,"style":1864},[1863],[1807,3987,1926],{"className":3988},[1868,3989],"mathbf",[1807,3991,3918],{"className":3992},[1874],[1807,3994,3996],{"className":3995},[1868,3155],[1807,3997,3999],{"className":3998},[2667],[1807,4000,4002],{"className":4001},[2672],[1807,4003,4005,4013],{"className":4004,"style":3366},[2676],[1807,4006,4007,4010],{"style":3167},[1807,4008],{"className":4009,"style":2685},[2684],[1807,4011,3646],{"className":4012,"style":3704},[1868,1869],[1807,4014,4017,4020],{"className":4015,"style":4016},[3177],"width:calc(100% - 0.0556em);margin-left:0.0556em;top:-3.4306em;",[1807,4018],{"className":4019,"style":2685},[2684],[1807,4021,4022],{"style":3262},[3186,4023,4024],{"xmlns":3188,"width":3189,"height":3265,"viewBox":3266,"preserveAspectRatio":3192},[3194,4025],{"d":3269},[1807,4027,1922],{"className":4028},[1874],[1807,4030,2285],{"className":4031},[1868,1869],[1807,4033,1929],{"className":4034},[1904],[1807,4036],{"className":4037,"style":1883},[1882],[1807,4039,3933],{"className":4040},[1887],[1807,4042],{"className":4043,"style":1883},[1882],[1807,4045,4047,4050,4053,4056,4059,4063],{"className":4046},[1859],[1807,4048],{"className":4049,"style":1864},[1863],[1807,4051,2431],{"className":4052},[1868,1869],[1807,4054,1922],{"className":4055},[1874],[1807,4057,2285],{"className":4058},[1868,1869],[1807,4060,4062],{"className":4061},[1904],")}",[1807,4064,2640],{"className":4065},[1868],[1796,4067,4068],{},"但有限名额、公平约束和不可逆伤害会改变优化问题。策略应在独立测试样本上评价：",[1807,4070,4072],{"className":4071},[2540],[1807,4073,4075,4138],{"className":4074},[1810],[1807,4076,4078],{"className":4077},[1814],[1816,4079,4080],{"xmlns":1818,"display":2549},[1820,4081,4082,4135],{},[1823,4083,4084,4087,4089,4091,4093,4095,4097,4099,4101,4103,4105,4107,4109,4111,4113,4115,4117,4119,4121,4123,4125,4127,4129,4131,4133],{},[1826,4085,4086],{},"V",[1830,4088,1922],{"stretchy":1832},[1826,4090,2418],{},[1830,4092,1929],{"stretchy":1832},[1830,4094,2282],{},[1826,4096,1828],{},[1830,4098,1833],{"stretchy":1832},[1826,4100,1836],{},[1830,4102,1922],{"stretchy":1832},[1826,4104,2418],{},[1830,4106,1922],{"stretchy":1832},[1826,4108,1842],{},[1830,4110,1929],{"stretchy":1832},[1830,4112,1929],{"stretchy":1832},[1830,4114,2162],{},[1826,4116,2431],{},[1830,4118,1922],{"stretchy":1832},[1826,4120,1842],{},[1830,4122,1929],{"stretchy":1832},[1826,4124,2418],{},[1830,4126,1922],{"stretchy":1832},[1826,4128,1842],{},[1830,4130,1929],{"stretchy":1832},[1830,4132,1845],{"stretchy":1832},[1826,4134,2640],{"mathvariant":2639},[1847,4136,4137],{"encoding":1849},"V(d)=E[Y(d(X))-c(X)d(X)].",[1807,4139,4141,4168,4207],{"className":4140,"ariaHidden":1855},[1854],[1807,4142,4144,4147,4150,4153,4156,4159,4162,4165],{"className":4143},[1859],[1807,4145],{"className":4146,"style":1864},[1863],[1807,4148,4086],{"className":4149,"style":1878},[1868,1869],[1807,4151,1922],{"className":4152},[1874],[1807,4154,2418],{"className":4155},[1868,1869],[1807,4157,1929],{"className":4158},[1904],[1807,4160],{"className":4161,"style":1883},[1882],[1807,4163,2282],{"className":4164},[1887],[1807,4166],{"className":4167,"style":1883},[1882],[1807,4169,4171,4174,4177,4180,4183,4186,4189,4192,4195,4198,4201,4204],{"className":4170},[1859],[1807,4172],{"className":4173,"style":1864},[1863],[1807,4175,1828],{"className":4176,"style":1870},[1868,1869],[1807,4178,1833],{"className":4179},[1874],[1807,4181,1836],{"className":4182,"style":1878},[1868,1869],[1807,4184,1922],{"className":4185},[1874],[1807,4187,2418],{"className":4188},[1868,1869],[1807,4190,1922],{"className":4191},[1874],[1807,4193,1842],{"className":4194,"style":1900},[1868,1869],[1807,4196,2484],{"className":4197},[1904],[1807,4199],{"className":4200,"style":1878},[1882],[1807,4202,2162],{"className":4203},[2209],[1807,4205],{"className":4206,"style":1878},[1882],[1807,4208,4210,4213,4216,4219,4222,4225,4228,4231,4234,4237],{"className":4209},[1859],[1807,4211],{"className":4212,"style":1864},[1863],[1807,4214,2431],{"className":4215},[1868,1869],[1807,4217,1922],{"className":4218},[1874],[1807,4220,1842],{"className":4221,"style":1900},[1868,1869],[1807,4223,1929],{"className":4224},[1904],[1807,4226,2418],{"className":4227},[1868,1869],[1807,4229,1922],{"className":4230},[1874],[1807,4232,1842],{"className":4233,"style":1900},[1868,1869],[1807,4235,2231],{"className":4236},[1904],[1807,4238,2640],{"className":4239},[1868],[1796,4241,4242],{},"“CATE 预测误差低”不保证政策价值高；错误可能恰好集中在决策阈值附近。",[2002,4244,4246],{"id":4245},"_6-数据泄漏与选择后推断","6. 数据泄漏与选择后推断",[1796,4248,4249],{},"典型泄漏包括：",[3594,4251,4252,4255,4258,4261,4264],{},[2012,4253,4254],{},"用处理后的变量预测倾向得分；",[2012,4256,4257],{},"在全样本标准化、筛特征后才切分；",[2012,4259,4260],{},"用同一结果挑选亚组并报告普通区间；",[2012,4262,4263],{},"尝试许多 learner，只展示效应最大的模型；",[2012,4265,4266],{},"把未来信息编码进历史特征。",[1796,4268,4269],{},"模型选择、超参数调优和亚组发现都属于分析过程。应使用嵌套交叉验证、独立确认样本、预注册或选择后推断。",[2002,4271,4273],{"id":4272},"_7-伦理与部署审计","7. 伦理与部署审计",[2032,4275,4276,4286],{},[2035,4277,4278],{},[2038,4279,4280,4283],{},[2041,4281,4282],{},"问题",[2041,4284,4285],{},"必须回答",[2051,4287,4288,4296,4304,4312,4320,4328],{},[2038,4289,4290,4293],{},[2056,4291,4292],{},"可识别性",[2056,4294,4295],{},"数据为何支持处理反事实？",[2038,4297,4298,4301],{},[2056,4299,4300],{},"重叠",[2056,4302,4303],{},"哪些人没有可比处理选择？",[2038,4305,4306,4309],{},[2056,4307,4308],{},"伤害",[2056,4310,4311],{},"错误分配的成本是否对称？",[2038,4313,4314,4317],{},[2056,4315,4316],{},"公平",[2056,4318,4319],{},"约束针对机会、结果还是误差？",[2038,4321,4322,4325],{},[2056,4323,4324],{},"可迁移性",[2056,4326,4327],{},"新地区的处理与人群是否相同？",[2038,4329,4330,4333],{},[2056,4331,4332],{},"监测",[2056,4334,4335],{},"部署后行为反应和数据漂移怎样发现？",[1796,4337,4338,4339,4343,4344,4351],{},"2024 年 ",[4340,4341,4342],"em",{},"Nature Medicine"," 的 ",[4345,4346,4350],"a",{"href":4347,"rel":4348},"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41591-024-02902-1",[4349],"nofollow","Feuerriegel 等"," 强调，因果机器学习从问题定义到临床转化需要连续审查；方法综述本身不是任何治疗有效性的证据。",[2002,4353,4355],{"id":4354},"_8-最低报告标准","8. 最低报告标准",[3594,4357,4358,4361,4364,4367,4370,4373,4376,4379],{},[2012,4359,4360],{},"目标参数与识别假设；",[2012,4362,4363],{},"所有特征的测量时点；",[2012,4365,4366],{},"训练、调参、交叉拟合和测试划分；",[2012,4368,4369],{},"nuisance learner 与性能；",[2012,4371,4372],{},"倾向得分和重叠诊断；",[2012,4374,4375],{},"ATE\u002FCATE 的区间、校准和稳定性；",[2012,4377,4378],{},"策略价值的样本外评估；",[2012,4380,4381],{},"软件版本、随机种子与完整分析日志。",[2002,4383,4384],{"id":4384},"课堂任务",[1796,4386,4387],{},"设计“把有限辅导名额分给学生”的政策学习方案：",[2009,4389,4390,4393,4396,4399,4402],{},[2012,4391,4392],{},"区分辍学风险与辅导 CATE；",[2012,4394,4395],{},"写出一个资源约束下的价值函数；",[2012,4397,4398],{},"指出两个不能作为处理前特征的变量；",[2012,4400,4401],{},"设计独立评估或随机上线实验；",[2012,4403,4404],{},"说明公平约束会怎样改变最优规则。",[2002,4406,4407],{"id":4407},"核心阅读",[3594,4409,4410,4419,4427,4435],{},[2012,4411,4412,4413,4418],{},"Chernozhukov et al. (2018), ",[4345,4414,4417],{"href":4415,"rel":4416},"https:\u002F\u002Fdoi.org\u002F10.1111\u002Fectj.12097",[4349],"“Double\u002FDebiased Machine Learning for Treatment and Structural Parameters”","。",[2012,4420,4421,4422,4418],{},"Wager & Athey (2018), ",[4345,4423,4426],{"href":4424,"rel":4425},"https:\u002F\u002Fdoi.org\u002F10.1080\u002F01621459.2017.1319839",[4349],"“Estimation and Inference of Heterogeneous Treatment Effects Using Random Forests”",[2012,4428,4429,4430,4418],{},"Athey & Wager (2021), ",[4345,4431,4434],{"href":4432,"rel":4433},"https:\u002F\u002Fdoi.org\u002F10.3982\u002FECTA15732",[4349],"“Policy Learning with Observational Data”",[2012,4436,4437,4438,4418],{},"Feuerriegel et al. (2024), ",[4345,4439,4442],{"href":4440,"rel":4441},"https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41591-024-02902-1",[4349],"“Causal Machine Learning for Predicting Treatment Outcomes”",[1796,4444,4445,4446,4450,4451,4418],{},"上一章：",[4345,4447,4449],{"href":4448},"..\u002F10-synthetic-control\u002F","合成控制法","｜下一章：",[4345,4452,4454],{"href":4453},"..\u002F12-frontier-literature-2026","前沿文献与现代案例",{"title":10,"searchDepth":4456,"depth":4456,"links":4457},2,[4458,4459,4460,4461,4462,4463,4464,4465,4466,4467,4468],{"id":2004,"depth":4456,"text":2004},{"id":2029,"depth":4456,"text":2030},{"id":2532,"depth":4456,"text":2533},{"id":3610,"depth":4456,"text":3611},{"id":3627,"depth":4456,"text":3628},{"id":3838,"depth":4456,"text":3839},{"id":4245,"depth":4456,"text":4246},{"id":4272,"depth":4456,"text":4273},{"id":4354,"depth":4456,"text":4355},{"id":4384,"depth":4456,"text":4384},{"id":4407,"depth":4456,"text":4407},"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。","md",{"sidebar":4472},{"order":4473},11,true,{"title":1534,"description":4469},"9EK8VmCA3Ew9VUJVCIfvoo51RrPoWI4qS59f9-XKf5o",[4478,4480],{"title":1528,"path":1529,"stem":1530,"description":4479,"children":-1},"为单个处理单位构造加权反事实，掌握供体池、处理前拟合、安慰剂推断与现代扩展。",{"title":1540,"path":1541,"stem":1542,"description":4481,"children":-1},"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。",1785754752268]