Sammenlign metoder

	Politikkevaluering ved invers sannsynlighetsevektning	Propensity Score Matching
Fagfelt	Kausal inferens	Forskningsstatistikk
Familie	Regression model	Process / pipeline
Opprinnelsesår	1952 (IPW origin); 2000s (policy evaluation application)	1983
Opphavsperson	Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)	Paul Rosenbaum and Donald Rubin
Type	Reweighting estimator for causal policy analysis	Method
Opprinnelig kilde	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.	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.
Alias	IPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weighting	PSM, propensity score weighting, covariate balance
Relaterte	6	3
Sammendrag	Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.	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.