Methoden vergleichen

	Politikevaluation mittels Propensity Score Matching	Propensity Score Matching
Fachgebiet	Kausale Inferenz	Forschungsstatistik
Familie	Regression model	Process / pipeline
Entstehungsjahr	1983; policy evaluation adaptation 1997	1983
Urheber	Rosenbaum & Rubin (1983); Heckman, Ichimura & Todd (1997) for program/policy evaluation application	Paul Rosenbaum and Donald Rubin
Typ	Quasi-experimental matching estimator	Method
Wegweisende Quelle	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.	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	PSM policy evaluation, policy PSM, propensity matching for program evaluation, PSM treatment evaluation	PSM, propensity score weighting, covariate balance
Verwandt	6	3
Zusammenfassung	Policy evaluation propensity score matching applies the propensity score framework — originally developed by Rosenbaum and Rubin (1983) and operationalized for program evaluation by Heckman et al. (1997) — to estimate the causal effect of a policy intervention. It constructs a credible comparison group from non-participants by matching them to participants on their estimated probability of receiving the treatment, enabling unbiased effect estimation without random assignment.	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.