Methoden vergelijken

	Marginaal Structureel Model in Onderwijsonderzoek	Propensity Score Matching
Vakgebied	Causale inferentie	Onderzoeksstatistiek
Familie	Regression model	Process / pipeline
Jaar van ontstaan	2000 (method); 2006 (canonical education application)	1983
Grondlegger	James M. Robins, Miguel A. Hernán, Babette Brumback (epidemiology); Guanglei Hong & Stephen Raudenbush (education application)	Paul Rosenbaum and Donald Rubin
Type	Causal inference / weighted regression model	Method
Oorspronkelijke bron	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Aliassen	MSM, marginal structural model, MSM with inverse probability weighting, IPW-MSM	PSM, propensity score weighting, covariate balance
Verwant	5	3
Samenvatting	A marginal structural model (MSM) is a causal inference technique that uses inverse probability weighting to estimate the effect of a treatment or educational intervention that changes over time. Introduced by Robins, Hernán and Brumback (2000) in epidemiology and brought into education by Hong and Raudenbush (2006), MSMs handle time-varying confounding — a challenge that conventional regression cannot resolve.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.