Compară metode

	Potrivirea dinamică a scorului de propensitate	Model Structural Marginal (MSM)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	1986-2010	2000
Autorul original	Robins (1986) on sequential treatments; Lechner & Miquel (2010) on dynamic matching	James M. Robins, Miguel A. Hernan, Babette Brumback
Tip	Sequential causal matching	Causal model / semiparametric weighting
Sursa seminală	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.
Denumiri alternative	dynamic PSM, sequential propensity score matching, longitudinal propensity matching, DPSM	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Înrudite	6	5
Rezumat	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.