Salīdzināt metodes

	Dinamiskā tendences rādītāja saskaņošana	Marginal Structural Model (MSM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1986-2010	2000
Autors	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching	James M. Robins, Miguel A. Hernan, Babette Brumback
Tips	Sequential causal matching	Causal model / semiparametric weighting
Pirmavots	Lechner, M., & Miquel, R. (2010). Identification of the effects of dynamic treatments by sequential conditional independence assumptions. Empirical Economics, 39(1), 111-137.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Citi nosaukumi	dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Saistītās	6	5
Kopsavilkums	Dynamic Propensity Score Matching (DPSM) extends classic propensity score matching to settings where treatment is assigned repeatedly over time and earlier treatment choices influence later ones. It estimates the causal effect of entire treatment sequences or regime changes by constructing matched comparisons at each decision point using the full history of covariates and prior treatments.	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.