Uporedite metode

	Mašinsko učenje-augmentovano uparivanje rezultata sklonosti	Izračunavanje dvostruke robustnosti (AIPW)
Oblast	Kauzalno zaključivanje	Kauzalno zaključivanje
Porodica	Regression model	Regression model
Godina nastanka	2004	2005
Tvorac	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Robins & Rotnitzky; Bang & Robins
Tip	Causal inference / matching	Semiparametric causal estimator
Temeljni izvor	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.	Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.
Drugi nazivi	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Srodne	6	5
Sažetak	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).	Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.