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| Propensity Weighting in Criminology× | Penimbang Kebarangkalian Songsang (IPW / IPTW)× | |
|---|---|---|
| Bidang≠ | Criminology | Inferens Kausal |
| Keluarga≠ | Process / pipeline | Regression model |
| Tahun asal≠ | 1983 | 2000 |
| Pengasas≠ | Paul R. Rosenbaum & Donald B. Rubin (propensity score); Robert Apel & Gary Sweeten (criminological adaptation) | Robins, Hernán & Brumback |
| Jenis≠ | Observational causal estimator for justice exposures | Causal inference weighting estimator |
| Sumber perintis≠ | 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. DOI ↗ | Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560. DOI ↗ |
| Alias≠ | IPTW for Justice Exposures, Inverse-Probability Weighting in Criminology, Propensity-Weighted Crime Effects, Observational Treatment-Effect Weighting | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Berkaitan≠ | 4 | 5 |
| Ringkasan≠ | 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. | Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias. |
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