Methoden vergelijken

	Multi-period Inverse Probability Weighting	Panel Data Inverse Probability Weighting
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2000	2000
Grondlegger	Robins, Hernan & Brumback	Robins, Hernan & Brumback
Type	Weighted causal estimator	Reweighting / causal inference
Oorspronkelijke bron	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., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Aliassen	longitudinal IPW, multi-period IPW, time-varying IPW, sequential IPW	panel IPW, longitudinal IPW, time-varying IPW, panel IPTW
Verwant	6	5
Samenvatting	Multi-period Inverse Probability Weighting (IPW) estimates the causal effect of a treatment that varies across multiple time periods by reweighting observations according to the probability of receiving each period's treatment given past treatment history and time-varying confounders. It creates a pseudo-population where treatment at each period is independent of measured confounders, enabling unbiased estimation of sustained treatment strategies.	Panel Data Inverse Probability Weighting (panel IPW) estimates the causal effect of a time-varying treatment by reweighting observed units to create a pseudo-population in which treatment is independent of measured confounders at each time point. It extends the cross-sectional IPW framework to longitudinal settings where treatment status and confounders both evolve across multiple periods.