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التقدير المتين المزدوج (AIPW)×تحليل الوساطة السببية (التأثيرات المباشرة وغير المباشرة الطبيعية)×
المجالالاستدلال السببيالاستدلال السببي
العائلةRegression modelRegression model
سنة النشأة20052010
صاحب الطريقةRobins & Rotnitzky; Bang & RobinsPearl (2001); general framework by Imai, Keele & Tingley (2010)
النوعSemiparametric causal estimatorCounterfactual causal decomposition
المصدر التأسيسيRobins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗Pearl, J. (2001). Direct and Indirect Effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI), 411-420. link ↗
الأسماء البديلةAIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)natural direct effect, natural indirect effect, NDE / NIE decomposition, counterfactual mediation
ذات صلة55
الملخصDoubly 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.Causal mediation analysis is a counterfactual framework that splits a treatment's total effect into a Natural Direct Effect (NDE) and a Natural Indirect Effect (NIE) that runs through a mediator. The modern general approach was formalised by Pearl (2001) and Imai, Keele and Tingley (2010), giving the decomposition a precise causal interpretation.
ScholarGateمجموعة البيانات
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  2. 2 المصادر
  3. PUBLISHED
  1. v1
  2. 2 المصادر
  3. PUBLISHED

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ScholarGateقارن الطرق: Doubly Robust Estimation · Causal Mediation Analysis. استُرجع بتاريخ 2026-06-17 من https://scholargate.app/ar/compare