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| 反実仮想による影響評価(CIE)× | 合成コントロール法(SCM)× | |
|---|---|---|
| 分野 | 因果推論 | 因果推論 |
| 系統 | Regression model | Regression model |
| 提唱年≠ | 1970s–2000s | 2003–2010 |
| 提唱者≠ | Heckman, Imbens, Rubin, and the program evaluation literature | Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010) |
| 種類≠ | Causal inference / program evaluation | Quasi-experimental causal inference |
| 原典≠ | Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874. DOI ↗ | Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗ |
| 別名 | CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation | SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method |
| 関連≠ | 5 | 4 |
| 概要≠ | Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice. | The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect. |
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