手法を比較

	条件付きプロセス分析（媒介変数の調整）	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	因果推論
系統	Regression model	Regression model	Regression model
提唱年	2018	2010	2008
提唱者	Andrew F. Hayes (PROCESS framework); Preacher, Rucker & Hayes (moderated mediation)	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
種類	Regression-based conditional process model	Counterfactual causal decomposition	Quasi-experimental causal design
原典	Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2nd ed.). The Guilford Press. ISBN: 978-1462534654	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	moderated mediation, moderated mediation analysis, PROCESS model, Hayes PROCESS conditional process model	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
関連	5	5	5
概要	Conditional process analysis is Andrew F. Hayes's regression-based PROCESS framework (2018) that combines mediation and moderation in a single model, testing how an indirect effect changes across levels of a moderator. It quantifies conditional indirect and conditional direct effects and tests them with bootstrap confidence intervals.	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.