Linganisha mbinu

	Uzani wa Athari ya Matibabu Tofauti (HTE-IPW)	Uzito wa Kinyume wa Uwezekano wa Matibabu (IPW / IPTW)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2003–2015	2000
Mwanzilishi	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins, Hernán & Brumback
Aina	Causal inference / weighted regression	Causal inference weighting estimator
Chanzo asilia	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.
Majina mbadala	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Zinazohusiana	5	5
Muhtasari	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.