Salīdzināt metodes

	Divu robota novērtējums politikas novērtēšanā	Marginal Structural Model (MSM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1994-2005	2000
Autors	Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tips	Semiparametric causal estimator	Causal model / semiparametric weighting
Pirmavots	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	DR estimation for policy, augmented IPW for policy evaluation, AIPW policy evaluation, doubly robust policy analysis	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Saistītās	5	5
Kopsavilkums	Policy Evaluation Doubly Robust Estimation applies the doubly robust (DR) estimator to assess the causal effect of a public policy or programme. It combines a model of treatment assignment (propensity score) with a model of the outcome, and requires only one of the two models to be correctly specified to produce a consistent estimate of the average treatment effect, making it a resilient tool for programme evaluation.	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.