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| Machine Learning-Augmented Doubly Robust Estimation (ML-DR)× | Propensity Score Weighting (PSW / IPW)× | |
|---|---|---|
| Fachgebiet | Kausale Inferenz | Kausale Inferenz |
| Familie | Regression model | Regression model |
| Entstehungsjahr≠ | 2018 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Urheber≠ | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Typ≠ | Semiparametric causal estimator with ML nuisance | Causal inference / reweighting |
| Wegweisende Quelle≠ | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. DOI ↗ | Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55. DOI ↗ |
| Aliasnamen | ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners. | Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003). |
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