手法を比較

	因果媒介分析（自然直接効果および自然間接効果）	回帰不連続デザイン（Regression Discontinuity Design, RDD）
分野	因果推論	因果推論
系統	Regression model	Regression model
提唱年	2010	2008
提唱者	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
種類	Counterfactual causal decomposition	Quasi-experimental causal design
原典	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
別名	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
関連	5	5
概要	Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.