Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Estimador de emparejamiento para la evaluación de políticas× | Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 1998-2006 | 2000 |
| Autor original≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Robins, Hernán & Brumback |
| Tipo≠ | Non-parametric causal estimator | Causal inference weighting estimator |
| Fuente seminal≠ | 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 |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | 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. |
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