قارن الطرق

	الترجيح المُعزَّز بالتعلم الآلي لدرجة الميل	الترجيح بالدرجة الميولية (PSW / IPW)
المجال	الاستدلال السببي	الاستدلال السببي
العائلة	Regression model	Regression model
سنة النشأة	2010–2018	1983 (propensity score); 2003 (efficient IPW estimator)
صاحب الطريقة	Lee, Lessler & Stuart (2010); Chernozhukov et al. (2018, DML framework)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
النوع	Causal inference / semiparametric weighting	Causal inference / reweighting
المصدر التأسيسي	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	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-PSW, ML-augmented IPW, machine learning propensity weighting, nonparametric propensity score weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
ذات صلة	5	6
الملخص	Machine learning-augmented propensity score weighting (ML-PSW) replaces logistic regression with flexible ML algorithms — such as gradient boosting, LASSO, or random forests — to estimate the propensity score, then uses inverse probability weights to balance treated and control groups. This reduces model-misspecification bias when the true relationship between covariates and treatment assignment is complex or high-dimensional.	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).