Võrdle meetodeid

	Masinõppimisega täiustatud sobivusskoori sobitamine	Propensity Score Weighting (PSW / IPW)
Valdkond	Põhjuslik järeldamine	Põhjuslik järeldamine
Perekond	Regression model	Regression model
Tekkeaasta	2004	1983 (propensity score); 2003 (efficient IPW estimator)
Looja	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tüüp	Causal inference / matching	Causal inference / reweighting
Algallikas	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.
Rööpnimetused	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	PSW, inverse probability weighting, IPW, propensity-based weighting
Seotud	6	6
Kokkuvõte	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).