Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Machine learning-augmented propensity score weighting× | Uzito wa Alama ya Mwelekeo (PSW / IPW)× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2010–2018 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Mwanzilishi≠ | Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Aina≠ | Causal inference / semiparametric weighting | Causal inference / reweighting |
| Chanzo asilia≠ | 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 ↗ |
| Majina mbadala | ML-PSW, ML-augmented IPW, machine learning propensity weighting, nonparametric propensity score weighting | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Zinazohusiana≠ | 5 | 6 |
| Muhtasari≠ | Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional. | 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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