Linganisha mbinu

	Ukadiriaji Imara Mara Mbili wa Athari Tofauti za Matibabu	Mfumo wa Kielelezo wa Uhusiano (MSM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2018-2023	2000
Mwanzilishi	Kennedy (2023); building on Robins, Rotnitzky & Zhao (1994) and Chernozhukov et al. (2018)	James M. Robins, Miguel A. Hernan, Babette Brumback
Aina	Semiparametric causal inference	Causal model / semiparametric weighting
Chanzo asilia	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.
Majina mbadala	DR-HTE, augmented IPW for HTE, doubly robust CATE estimation, semiparametric HTE estimation	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Zinazohusiana	5	5
Muhtasari	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.