Bandingkan kaedah

	Penimbang Kebolehjadian Songsangan Dinamik	Penimbang Kebarangkalian Songsang (IPW / IPTW)
Bidang	Inferens Kausal	Inferens Kausal
Keluarga	Regression model	Regression model
Tahun asal	1986-2000	2000
Pengasas	James M. Robins and colleagues	Robins, Hernán & Brumback
Jenis	Causal weighting estimator	Causal inference weighting estimator
Sumber perintis	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.
Alias	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Berkaitan	4	5
Ringkasan	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.