Linganisha mbinu

	Propensity Weighting in Criminology	Propensity Score Methods in Criminology
Nyanja	Criminology	Criminology
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1983	1983
Mwanzilishi	Paul R. Rosenbaum & Donald B. Rubin (propensity score); Robert Apel & Gary Sweeten (criminological adaptation)	Paul Rosenbaum & Donald Rubin (method); Apel & Sweeten (criminological application)
Aina	Observational causal estimator for justice exposures	Observational causal-inference technique applied to crime and justice interventions
Chanzo asilia	Apel, R. J., & Sweeten, G. (2010). Propensity score matching in criminology and criminal justice. In A. R. Piquero & D. Weisburd (Eds.), Handbook of Quantitative Criminology (pp. 543–562). Springer.	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.
Majina mbadala	IPTW for Justice Exposures, Inverse-Probability Weighting in Criminology, Propensity-Weighted Crime Effects, Observational Treatment-Effect Weighting	Propensity Score Analysis in Crime and Justice Research, Criminological Propensity Score Matching, Observational Causal Inference in Criminology, Propensity Score Adjustment for Justice Interventions
Zinazohusiana	4	4
Muhtasari	Propensity weighting estimates the causal effect of a justice exposure — incarceration, gang membership, a program, or a sanction — from observational data when randomization was impossible. It models each unit's probability of receiving the exposure given measured confounders (the propensity score) and then weights units by the inverse of that probability, creating a pseudo-population in which the exposure is unrelated to those confounders. Rosenbaum and Rubin introduced the propensity score in 1983, and Apel and Sweeten adapted it for criminology, where ethical and practical barriers make experiments rare.	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.