方法证据记录
Machine Learning-Augmented Propensity Score Matching
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).
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Machine Learning-Augmented Propensity Score Matching Estimator
分类方法记录 · regression-model / causal-inference
- 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 10.1037/1082-989X.9.4.403
- Westreich, D., Lessler, J., & Funk, M. J. (2010). Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. Journal of Clinical Epidemiology, 63(8), 826-833. · DOI 10.1016/j.jclinepi.2009.11.020
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