Comparer des méthodes

	Propensity Score Methods in Criminology	Recidivism Survival Analysis
Domaine	Criminology	Criminology
Famille	Process / pipeline	Survival analysis
Année d'origine	1983	1988
Auteur d'origine	Paul Rosenbaum & Donald Rubin (method); Apel & Sweeten (criminological application)	David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application)
Type	Observational causal-inference technique applied to crime and justice interventions	Time-to-event regression for reoffending
Source fondatrice	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.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.
Alias	Propensity Score Analysis in Crime and Justice Research, Criminological Propensity Score Matching, Observational Causal Inference in Criminology, Propensity Score Adjustment for Justice Interventions	Time-to-Recidivism Modeling, Recidivism Hazard Modeling, Failure-Time Analysis of Reoffending, Survival Analysis of Reoffending
Apparentées	4	4
Résumé	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.	Recidivism survival analysis models the time from a release or index event until an individual reoffends, treating reoffending as a time-to-event ('failure') outcome with censoring for those not observed to fail. It applies survival methods — Kaplan-Meier curves, Cox proportional-hazards regression, and split-population models — to answer not just whether someone recidivates but how quickly and what raises or lowers that risk over time.