手法を比較

	政策評価における傾向スコアマッチング	二重に頑健な推定量（AIPW）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	1983; policy evaluation adaptation 1997	2005
提唱者	Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application	Robins & Rotnitzky; Bang & Robins
種類	Quasi-experimental matching estimator	Semiparametric causal estimator
原典	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
別名	PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
関連	6	5
概要	Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.