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| Machine Learning-Augmented Inverse Probability Weighting× | Penimbang Skor Kecenderungan (PSW / IPW)× | |
|---|---|---|
| Bidang | Inferens Kausal | Inferens Kausal |
| Keluarga | Regression model | Regression model |
| Tahun asal≠ | 2003-2018 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Pengasas≠ | Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Jenis≠ | Semiparametric causal estimator | Causal inference / reweighting |
| Sumber perintis≠ | 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 ↗ |
| Alias | ML-IPW, nonparametric IPW, data-adaptive IPW, ML-augmented propensity weighting | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Berkaitan≠ | 5 | 6 |
| Ringkasan≠ | Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting, ML-IPW controls for high-dimensional and nonlinear confounders that classical IPW misses, while retaining the intuitive weighting framework. | 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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