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Dziedzina	Wnioskowanie przyczynowe	Statystyka w badaniach
Rodzina	Regression model	Process / pipeline
Rok powstania	2004	1983
Twórca	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Paul Rosenbaum and Donald Rubin
Typ	Causal inference / matching	Method
Źródło pierwotne	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.	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.
Inne nazwy	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	PSM, propensity score weighting, covariate balance
Pokrewne	6	3
Podsumowanie	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 matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.