手法を比較

	動的な反事実的インパクト評価	傾向スコア重み付け（PSW / IPW）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	1986–2009	1983 (propensity score); 2003 (efficient IPW estimator)
提唱者	Robins (1986); Lechner (2009) for sequential treatment settings	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
種類	Causal inference / program evaluation	Causal inference / reweighting
原典	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.	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.
別名	dynamic CIE, dynamic treatment evaluation, time-varying counterfactual analysis, longitudinal counterfactual evaluation	PSW, inverse probability weighting, IPW, propensity-based weighting
関連	6	6
概要	Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).