방법 비교

	방향성 비순환 그래프(DAG)를 이용한 인과 관계 식별(do-calculus)	역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2009	2000
창시자	Judea Pearl	Robins, Hernán & Brumback
유형	Causal identification framework	Causal inference weighting estimator
원전	Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press. ISBN: 978-0521895606	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
별칭	do-calculus, backdoor adjustment, Pearl causal identification, DAG ile Nedensel Tanımlama (do-calculus)	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
관련	5	5
요약	DAG causal identification is a framework, developed by Judea Pearl (2009), that encodes causal assumptions as a directed acyclic graph and uses the do-calculus rules to determine whether and how a causal effect can be identified from observational data. It systematically handles confounders, instrumental variables, and backdoor paths.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.