Salīdzināt metodes

	Dinamiskā apgrieztā varbūtības svēršana	Aproksimēta novērtēšana (PSW / IPW)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1986-2000	1983 (propensity score); 2003 (efficient IPW estimator)
Autors	James M. Robins and colleagues	Rosenbaum & Rubin (propensity score); Hirano, Imbens & Ridder (efficient weighting)
Tips	Causal weighting estimator	Causal inference / reweighting
Pirmavots	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41-55.
Citi nosaukumi	Dynamic IPW, Time-varying IPW, Longitudinal IPW, Sequential IPW	PSW, inverse probability weighting, IPW, propensity-based weighting
Saistītās	4	6
Kopsavilkums	Dynamic Inverse Probability Weighting (Dynamic IPW) estimates the causal effect of a time-varying treatment sequence by reweighting observed data to mimic a hypothetical randomised trial. Developed by Robins and colleagues in the context of marginal structural models, it handles the challenge that in longitudinal settings, past treatment affects future covariates, which in turn affect future treatment — a feedback loop that standard regression cannot untangle.	Propensity score weighting is a causal-inference method that reweights observations so that the covariate distributions of treated and untreated units look exchangeable, enabling unbiased estimation of average treatment effects from observational data. Each unit receives a weight that is the inverse of its probability of receiving the treatment it actually received — a strategy formalised by Rosenbaum and Rubin (1983) and given its efficient semiparametric form by Hirano, Imbens and Ridder (2003).