השוואת שיטות

	משקל הסתברות טיפול הטרוגני הפוך (HTE-IPW)	משקולות הסתברות הפוכות (IPW / IPTW)
תחום	הסקה סיבתית	הסקה סיבתית
משפחה	Regression model	Regression model
שנת המקור	2003–2015	2000
הוגה השיטה	Hirano, Imbens & Ridder; further developed by Abrevaya, Hsu & Lieli	Robins, Hernán & Brumback
סוג	Causal inference / weighted regression	Causal inference weighting estimator
מקור מכונן	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.
כינויים	HTE-IPW, CATE-IPW, heterogeneous IPW, conditional effect IPW	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
קשורות	5	5
תקציר	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.