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	विषम उपचार प्रभाव व्युत्क्रम संभाव्यता भारण (HTE-IPW)	उपचार भारण की व्युत्क्रम प्रायिकता (IPW / IPTW)
क्षेत्र	कारणात्मक अनुमान	कारणात्मक अनुमान
परिवार	Regression model	Regression model
उद्भव वर्ष	2003–2015	2000
प्रवर्तक	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins, Hernán & Brumback
प्रकार	Causal inference / weighted regression	Causal inference weighting estimator
मौलिक स्रोत	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. Econometrica, 71(4), 1161-1189.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
उपनाम	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
संबंधित	5	5
सारांश	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.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.