পদ্ধতির তুলনা করুন

	বিষমধর্মী ট্রিটমেন্ট ইফেক্ট ইনভার্স প্রোবাবিলিটি ওয়েটিং (HTE-IPW)	প্রোপেনসিটি স্কোর ওয়েটিং (PSW / IPW)
ক্ষেত্র	কার্যকারণ অনুমান	কার্যকারণ অনুমান
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2003–2015	1983 (propensity score); 2003 (efficient IPW estimator)
প্রবর্তক	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
ধরন	Causal inference / weighted regression	Causal inference / reweighting
মৌলিক উৎস	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.	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.
অপর নাম	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
সম্পর্কিত	5	6
সারসংক্ষেপ	HTE-IPW extends standard inverse probability weighting to recover how causal effects vary across subgroups or covariate values. By reweighting each observation by the inverse of its estimated treatment probability, the method creates a pseudo-population in which treatment is independent of background characteristics, and then estimates conditional average treatment effects (CATEs) as a function of those characteristics.	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).