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	Propensity Score Methods in Criminology	Padanan Skor Kecenderungan
Bidang	Criminology	Statistik Penyelidikan
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	1983	1983
Pengasas	Paul Rosenbaum & Donald Rubin (method); Apel & Sweeten (criminological application)	Paul Rosenbaum and Donald Rubin
Jenis	Observational causal-inference technique applied to crime and justice interventions	Method
Sumber perintis	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	PSM, propensity score weighting, covariate balance
Berkaitan	4	3
Ringkasan	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 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.