Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Estimador de emparejamiento robusto (emparejamiento con corrección de sesgo)× | Ponderación por Probabilidad Inversa de Tratamiento (IPW / IPTW)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2006/2011 | 2000 |
| Autor original≠ | Abadie & Imbens | Robins, Hernán & Brumback |
| Tipo≠ | Causal inference / matching | Causal inference weighting estimator |
| Fuente seminal≠ | Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11. 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≠ | bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching | IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates. | 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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