Jämför metoder

	Propensity Score Methods in Criminology	Propensitetspoängsviktning (PSW / IPW)
Ämnesområde	Criminology	Kausal inferens
Familj	Process / pipeline	Regression model
Ursprungsår	1983	1983 (propensity score); 2003 (efficient IPW estimator)
Upphovsperson	Paul Rosenbaum & Donald Rubin (method); Apel & Sweeten (criminological application)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Typ	Observational causal-inference technique applied to crime and justice interventions	Causal inference / reweighting
Ursprungskälla	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.
Alias	Propensity Score Analysis in Crime and Justice Research, Criminological Propensity Score Matching, Observational Causal Inference in Criminology, Propensity Score Adjustment for Justice Interventions	PSW, inverse probability weighting, IPW, propensity-based weighting
Närliggande	4	6
Sammanfattning	Propensity score methods estimate the causal effect of a criminal-justice treatment — such as incarceration, gang membership, a diversion program, or arrest — from observational data, where random assignment is impossible. Building on Rosenbaum and Rubin's 1983 framework and adapted to crime research by Apel, Sweeten, and others, the approach summarizes many confounders into a single probability of treatment, then matches, weights, or stratifies on it to approximate a randomized comparison. This page covers the criminological application; for the general estimators see propensity-score-matching and propensity-score-weighting.	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).