Compara mètodes

	Estimació robusta a doble augmentada per aprenentatge automàtic (ML-DR)	Model Estructural Marginal (MSM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2018	2000
Autor original	Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins	James M. Robins, Miguel A. Hernan, Babette Brumback
Tipus	Semiparametric causal estimator with ML nuisance	Causal model / semiparametric weighting
Font seminal	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550-560.
Àlies	ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR	MSM, MSM-IPTW, marginal structural Cox model, weighted structural model
Relacionats	6	5
Resum	Machine learning-augmented doubly robust (ML-DR) estimation combines the classical doubly robust (AIPW) identification strategy with flexible machine learning models for the nuisance functions — the propensity score and the outcome regression. The result is a causal estimator that is consistent if either ML component is correctly specified, and that achieves valid, root-n inference even when the nuisance models are estimated with high-dimensional regularisation or nonparametric learners.	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.