Confronta i metodi

	Stimatore a Corrispondenza Dinamica	Modello Strutturale Marginale (MSM)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	2010	2000
Ideatore	Lechner & Miquel (2010); building on Heckman, Ichimura & Todd (1998)	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipo	Nonparametric causal inference / matching	Causal model / semiparametric weighting
Fonte seminale	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.
Alias	dynamic treatment matching, sequential matching estimator, dynamic selection-on-observables, DME	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Correlati	6	5
Sintesi	The Dynamic Matching Estimator extends standard matching methods to settings where treatment is assigned sequentially over multiple periods. Instead of a single treatment decision, units receive or forgo treatment at each time point, and the estimator identifies causal effects of entire treatment histories by matching on time-varying covariates and past treatment paths, under sequential conditional independence assumptions.	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.