Сравнение на методи

	Двойно робастно оценяване на хетерогенен ефект от въздействие	Маргинален структурен модел (МСМ)
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)	James M. Robins, Miguel A. Hernan, Babette Brumback
Тип	Semiparametric causal inference	Causal model / semiparametric weighting
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., Hernan, 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	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
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.	A marginal structural model is a causal modeling framework designed to estimate the effect of a time-varying treatment in the presence of time-varying confounders that are themselves affected by prior treatment. By reweighting observations with inverse probability of treatment weights, MSMs create a pseudo-population in which confounding is eliminated, enabling unbiased estimation of causal treatment contrasts even when standard regression adjustments would fail.