Salīdzināt metodes

	Politikas novērtēšana: apgrieztās varbūtības svēršana	Marginal Structural Model (MSM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1952 (IPW origin); 2000s (policy evaluation application)	2000
Autors	Horvitz & Thompson (1952); extended to causal policy settings by Robins, Hernan & Brumback (2000) and Imbens & Wooldridge (2009)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tips	Reweighting estimator for causal policy analysis	Causal model / semiparametric weighting
Pirmavots	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	IPW policy evaluation, propensity-weighted policy analysis, inverse probability of treatment weighting	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Saistītās	6	5
Kopsavilkums	Policy evaluation inverse probability weighting (IPW) uses estimated propensity scores to reweight observed units so that the weighted sample mimics a randomised experiment. Each unit is weighted by the inverse of its probability of receiving the policy, creating a pseudo-population in which treatment assignment is independent of observed covariates and the average treatment effect (ATE) can be read off directly.	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.