Сравнение на методи

	Двойно робастно оценяване на хетерогенен ефект от въздействие	Претегляне с обратна вероятност на лечението (IPW / IPTW)
Област	Причинно-следствено заключение	Причинно-следствено заключение
Семейство	Regression model	Regression model
Година на възникване	2018-2023	2000
Създател	Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018)	Robins, Hernán & Brumback
Тип	Semiparametric causal inference	Causal inference weighting estimator
Основополагащ източник	Kennedy, E. H. (2023). Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics, 17(2), 3008-3049.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Други названия	DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Свързани	5	5
Резюме	Doubly robust estimation of heterogeneous treatment effects (HTE) estimates how the causal effect of a treatment varies across subgroups or individual covariate values. By combining an outcome model and a propensity score model, it retains consistency if either model is correctly specified, and supports flexible machine learning nuisance estimators through cross-fitting to produce valid conditional average treatment effect (CATE) estimates.	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.