Methoden vergleichen

	Dynamisches Propensity Score Matching	Propensity Score Weighting (PSW / IPW)
Fachgebiet	Kausale Inferenz	Kausale Inferenz
Familie	Regression model	Regression model
Entstehungsjahr	1986-2010	1983 (propensity score); 2003 (efficient IPW estimator)
Urheber	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Typ	Sequential causal matching	Causal inference / reweighting
Wegweisende Quelle	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	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.
Aliasnamen	dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM	PSW, inverse probability weighting, IPW, propensity-based weighting
Verwandt	6	6
Zusammenfassung	Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).