Appendix — Revision and Evidence Map
Reading and Software Map
Selective foundational and current literature with dated official implementation guidance
Reading and Software Map
How this map was assembled
This is a selective teaching map, not a systematic review. Links and software documentation were checked on 1 August 2026. Foundational readings establish estimands; recent readings address known failure modes. Live documentation can change, so record the version used in an empirical project.
Identification and inference
| Purpose | Reading | Teaching use |
|---|---|---|
| potential outcomes | Rubin (1974) | assignment mechanisms and causal effects |
| IV and LATE | Angrist, Imbens and Rubin (1996) | complier interpretation |
| weak instruments | Andrews, Stock and Sun (2019) | robust diagnostics and inference |
| clustering | Abadie et al. (2023) | when and why to cluster |
| OVB sensitivity | Cinelli and Hazlett (2020) | observed-variable benchmarking |
| IV sensitivity | Cinelli and Hazlett (2025) | exclusion and instrument-confounding violations |
| average-case sensitivity | Zhang and Zhao (2026) | average-strength rather than only worst-case confounding bounds |
Policy evaluation designs
| Design | Reading | Teaching use |
|---|---|---|
| applied DID | Card and Krueger (1994) | design reconstruction and historical debate |
| group-time DID | Callaway and Sant’Anna (2021) | heterogeneous staggered effects |
| imputation event study | Borusyak, Jaravel and Spiess (2024) | supported untreated-outcome imputation |
| DID sensitivity | Rambachan and Roth (2023) | violations of exact parallel trends |
| practical RDD | Cattaneo, Idrobo and Titiunik (2020) | local estimation and diagnostics |
| matching | Stuart (2010) | design before outcome analysis |
| overlap weights | Li, Morgan and Zaslavsky (2018) | target population under overlap |
| synthetic control | Abadie, Diamond and Hainmueller (2010) | transparent comparative case study |
| synthetic DID | Arkhangelsky et al. (2021) | unit and time weighting |
Flexible causal analysis and transport
| Purpose | Reading | Teaching use |
|---|---|---|
| double ML | Chernozhukov et al. (2018) | orthogonal scores and cross-fitting |
| generalized random forests | Athey, Tibshirani and Wager (2019) | local moments and heterogeneity |
| R-learner | Nie and Wager (2021) | residual-on-residual CATE learning |
| efficient policy learning | Athey and Wager (2021) | constrained policy value |
| broad 2024 review | Feuerriegel et al. (2024) | causal ML workflow and limitations |
| external validity framework | Egami and Hartman (2023) | population, treatment, outcome and context |
| transport review | Degtiar and Rose (2023) | assumptions and estimators |
Live software guidance
| Task | Official documentation | Audit note |
|---|---|---|
| group-time DID | did | declare control group, anticipation, aggregation and simultaneous bands |
| high-dimensional fixed effects/IV | fixest | inspect formula, fixed effects, clustering and current version |
| RDD | rdrobust | retain bandwidth, kernel and robust bias-correction output |
| synthetic DID | synthdid | documentation described beta status at the review date; verify design support |
| causal/generalized forests | grf | assess overlap, calibration and honest policy evaluation |
| double ML | DoubleML | record learners, tuning, folds, score and repeated-split stability |
Software names do not define estimands. Reconstruct the identification memo before translating any example code.
Reproducibility standards
- AEA Data and Code Availability Policy, current page reviewed against the February 2026 policy.
- AEA data and code guidance, including venue-specific resources and updates.
- AEA policy report (May 2026), useful context on implementation and institutional learning.
Reading protocol
For any paper, extract only six lines before reading its regression table:
- target estimand;
- assignment/counterfactual source;
- identifying assumptions;
- support and inference level;
- strongest design-specific diagnostic;
- population, treatment and time boundary.