השוואת שיטות

	אמידה חסונה כפולה (AIPW)	יער אקראי
תחום	הסקה סיבתית	למידת מכונה
משפחה	Regression model	Machine learning
שנת המקור	2005	2001
הוגה השיטה	Robins & Rotnitzky; Bang & Robins	Breiman, L.
סוג	Semiparametric causal estimator	Ensemble (bagging of decision trees)
מקור מכונן	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
כינויים	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
קשורות	5	4
תקציר	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.