Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Propensity Score Methods in Criminology× | Recidivism Survival Analysis× | |
|---|---|---|
| Área | Criminology | Criminology |
| Família≠ | Process / pipeline | Survival analysis |
| Ano de origem≠ | 1983 | 1988 |
| Autor original≠ | Paul Rosenbaum & Donald Rubin (method); Apel & Sweeten (criminological application) | David R. Cox (method); Peter Schmidt & Ann Dryden Witte (criminological application) |
| Tipo≠ | Observational causal-inference technique applied to crime and justice interventions | Time-to-event regression for reoffending |
| Fonte seminal≠ | 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. DOI ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202. DOI ↗ |
| Outros nomes | 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 |
| Relacionados | 4 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
|
|