পদ্ধতির তুলনা করুন

	দৃঢ় প্রতি-বাস্তব প্রভাব মূল্যায়ন	দ্বৈতভাবে সুদৃঢ় প্রাক্কলন (AIPW)
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2010s	2005
প্রবর্তক	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Robins & Rotnitzky; Bang & Robins
ধরন	Robustness-validated causal evaluation	Semiparametric causal estimator
মৌলিক উৎস	Bia, M., Flores, C. A., Flores-Lagunes, A., & Mattei, A. (2014). A Stata package for the application of semiparametric estimators of dose–response functions. Stata Journal, 14(3), 580–604.	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.
অপর নাম	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
সম্পর্কিত	5	5
সারসংক্ষেপ	Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-differences, and regression discontinuity — to ensure that conclusions do not depend on any single methodological choice.	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.