قارن الطرق

	مطابقة درجات الميل المعززة بالتعلم الآلي	الترجيح بالدرجة الميولية (PSW / IPW)
المجال	الاستدلال السببي	الاستدلال السببي
العائلة	Regression model	Regression model
سنة النشأة	2004	1983 (propensity score); 2003 (efficient IPW estimator)
صاحب الطريقة	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
النوع	Causal inference / matching	Causal inference / reweighting
المصدر التأسيسي	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.
الأسماء البديلة	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	PSW, inverse probability weighting, IPW, propensity-based weighting
ذات صلة	6	6
الملخص	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).