방법 비교

	인과적 매개 분석 (자연 직접 효과 및 간접 효과)	최소제곱법(OLS) 회귀	회귀 불연속 설계(Regression Discontinuity Design, RDD)
분야	인과추론	계량경제학	인과추론
계열	Regression model	Regression model	Regression model
기원 연도	2010	2019	2008
창시자	Pearl (2001); general framework by Imai, Keele & Tingley (2010)	Wooldridge (textbook treatment); classical least squares	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
유형	Counterfactual causal decomposition	Linear regression	Quasi-experimental causal design
원전	Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
별칭	natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
관련	5	5	5
요약	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).	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.