Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Estimador Robusto por Correspondência (Correspondência com Correção de Viés)× | Ponderação pela Probabilidade Inversa de Tratamento (IPW / IPTW)× | |
|---|---|---|
| Área | Inferência causal | Inferência causal |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2006/2011 | 2000 |
| Autor original≠ | Abadie & Imbens | Robins, Hernán & Brumback |
| Tipo≠ | Causal inference / matching | Causal inference weighting estimator |
| Fonte 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 ↗ |
| Outros nomes≠ | 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 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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