방법 비교

	기계 학습 증강 역확률 가중치 (ML-IPW)	성향 점수 가중치 (PSW / IPW)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	2003-2018	1983 (propensity score); 2003 (efficient IPW estimator)
창시자	Hirano, Imbens & Ridder (semiparametric foundation, 2003); Chernozhukov et al. (DML framework, 2018)	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
유형	Semiparametric causal estimator	Causal inference / reweighting
원전	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.
별칭	ML-IPW, nonparametric IPW, data-adaptive IPW, ML-augmented propensity weighting	PSW, inverse probability weighting, IPW, propensity-based weighting
관련	5	6
요약	Machine learning-augmented inverse probability weighting replaces parametric logistic regression with flexible ML algorithms to estimate treatment propensity scores, then reweights the sample to balance treated and control units. By leveraging data-adaptive learners such as lasso, random forests, or gradient boosting, ML-IPW controls for high-dimensional and nonlinear confounders that classical IPW misses, while retaining the intuitive weighting framework.	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).