Compara mètodes

	Avaluació d'Impact Contrafactual Multi-període	Model Estructural Marginal (MSM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2000s–2010s	2000
Autor original	Developed through EU policy evaluation practice (European Commission); formalized by Lechner, Caliendo, and related econometricians	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipus	Causal inference / quasi-experimental evaluation	Causal model / semiparametric weighting
Font seminal	Caliendo, M., & Kopeinig, S. (2008). Some Practical Guidance for the Implementation of Propensity Score Matching. Journal of Economic Surveys, 22(1), 31-72.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Àlies	multi-period CIE, longitudinal counterfactual evaluation, dynamic counterfactual impact evaluation, multi-wave CIE	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionats	4	5
Resum	Multi-period Counterfactual Impact Evaluation (CIE) estimates the causal effect of a policy or program by constructing what would have happened to treated units across multiple time periods had they not been treated. Unlike single-period evaluations, it tracks treatment effects as they evolve over time, capturing dynamic, delayed, or fading impacts that a two-period comparison would miss.	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.