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Regression modelQuasi-experimental / causal inference

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).

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Sources

  1. 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 ↗
  2. 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 ↗

Related methods

Coarsened Exact MatchingDoubly Robust EstimationEntropy BalancingMachine learning-augmented doubly robust estimationPropensity Score MatchingPropensity Score Weighting

Referenced by

Machine Learning-Augmented Coarsened Exact MatchingMachine learning-augmented doubly robust estimationMachine Learning-Augmented Inverse Probability WeightingMachine learning-augmented propensity score weighting

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ScholarGate — Machine Learning-Augmented Propensity Score Matching (Machine Learning-Augmented Propensity Score Matching Estimator). Retrieved 2026-06-04 from https://scholargate.app/en/causal-inference/machine-learning-augmented-propensity-score-matching