Compara mètodes

	Avaluació d'Impacte Contrafactual d'Efectes de Tractament Heterogenis	Model Estructural Marginal (MSM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2010s	2000
Autor original	Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipus	Quasi-experimental causal inference with subgroup heterogeneity	Causal model / semiparametric weighting
Font seminal	Cerulli, G. (2010). Modelling and measuring the effect of public subsidies on business R&D: A critical review of the econometric literature. Economic Record, 86(274), 421-449.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Àlies	HTE-CIE, heterogeneous CIE, CATE-based counterfactual evaluation, subgroup counterfactual impact evaluation	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionats	4	5
Resum	Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment characteristics. Rather than reporting a single average treatment effect, it maps the Conditional Average Treatment Effect (CATE) across the covariate space, revealing who benefits most or least from an intervention.	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.