Salīdzināt metodes

	Propensity Weighting in Criminology	Apgrieztā varbūtības svēršana (IPW / IPTW)
Nozare	Criminology	Cēloņsakarību secināšana
Saime	Process / pipeline	Regression model
Izcelsmes gads	1983	2000
Autors	Paul R. Rosenbaum & Donald B. Rubin (propensity score); Robert Apel & Gary Sweeten (criminological adaptation)	Robins, Hernán & Brumback
Tips	Observational causal estimator for justice exposures	Causal inference weighting estimator
Pirmavots	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.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	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
Saistītās	4	5
Kopsavilkums	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.