Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Estimateur par appariement pour l'évaluation des politiques× | Pondération par l'inverse de la probabilité de traitement (IPW / IPTW)× | |
|---|---|---|
| Domaine | Inférence causale | Inférence causale |
| Famille | Regression model | Regression model |
| Année d'origine≠ | 1998-2006 | 2000 |
| Auteur d'origine≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Robins, Hernán & Brumback |
| Type≠ | Non-parametric causal estimator | Causal inference weighting estimator |
| Source fondatrice≠ | Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267. 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≠ | matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation. | 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. |
| ScholarGateJeu de données ↗ |
|
|