Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ulinganifu wa Alama ya Mwelekeo Ulioimarishwa na Mashine (ML-PSM)× | Uzito wa Alama ya Mwelekeo (PSW / IPW)× | |
|---|---|---|
| Nyanja | Uhitimisho wa Kisababishi | Uhitimisho wa Kisababishi |
| Familia | Regression model | Regression model |
| Mwaka wa asili≠ | 2004 | 1983 (propensity score); 2003 (efficient IPW estimator) |
| Mwanzilishi≠ | McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010) | Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting) |
| Aina≠ | Causal inference / matching | Causal inference / reweighting |
| Chanzo asilia≠ | McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425. 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-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching | PSW, inverse probability weighting, IPW, propensity-based weighting |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT). | 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). |
| ScholarGateSeti ya data ↗ |
|
|