Bandingkan kaedah

	Anggaran Keboleh-Teguhan Berganda Pelbagai Tempoh	Penimbang Kebarangkalian Songsang (IPW / IPTW)
Bidang	Inferens Kausal	Inferens Kausal
Keluarga	Regression model	Regression model
Tahun asal	1994-2021	2000
Pengasas	Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021)	Robins, Hernán & Brumback
Jenis	Semiparametric causal estimator	Causal inference weighting estimator
Sumber perintis	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Alias	longitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Berkaitan	6	5
Ringkasan	Multi-period doubly robust (DR) estimation extends the classic doubly robust approach to longitudinal settings with multiple treatment periods and time points. It combines an outcome regression model and a propensity score model for each period, retaining consistency of the causal effect estimate as long as at least one of the two models is correctly specified at every time point.	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.