Linganisha mbinu

	Tathmini ya Athari Dhidi ya Ukweli Halisi Inayobadilika	Mfumo wa Kielelezo wa Uhusiano (MSM)
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	1986–2009	2000
Mwanzilishi	Robins (1986); Lechner (2009) for sequential treatment settings	James M. Robins, Miguel A. Hernan, Babette Brumback
Aina	Causal inference / program evaluation	Causal model / semiparametric weighting
Chanzo asilia	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Majina mbadala	dynamic CIE, dynamic treatment evaluation, time-varying counterfactual analysis, longitudinal counterfactual evaluation	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Zinazohusiana	6	5
Muhtasari	Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.	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.