방법 비교

	동적 역확률 가중치	역확률 가중치 (Inverse Probability Weighting, IPW / IPTW)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1986-2000	2000
창시자	James M. Robins and colleagues	Robins, Hernán & Brumback
유형	Causal weighting estimator	Causal inference weighting estimator
원전	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
별칭	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
관련	4	5
요약	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.	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.