手法を比較

	機械学習拡張差分-イン-差分（ML-DiD）	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	2018-2020	2003–2010
提唱者	Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
種類	Causal inference / semiparametric	Quasi-experimental causal inference
原典	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	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.
別名	ML-DiD, double/debiased ML DiD, DML difference-in-differences, augmented DiD	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
関連	6	4
概要	Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional, or nonlinear. The approach, rooted in double/debiased machine learning (Chernozhukov et al., 2018) and doubly-robust DiD (Sant'Anna & Zhao, 2020), guards against misspecification bias while preserving the core DiD logic of before-after, treated-versus-control comparisons.	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.