Methoden vergelijken

	Multi-period Doubly Robust Estimation	Marginaal Structureel Model (MSM)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	1994-2021	2000
Grondlegger	Robins, Rotnitzky, and Zhao; extended by Bang & Robins (2005) and Callaway & Sant'Anna (2021)	James M. Robins, Miguel A. Hernan, Babette Brumback
Type	Semiparametric causal estimator	Causal model / semiparametric weighting
Oorspronkelijke bron	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Aliassen	longitudinal DR estimation, multi-period DR, multi-wave doubly robust, sequential doubly robust estimation	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Verwant	6	5
Samenvatting	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.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.