Comparar métodos

	Análisis de sensibilidad aumentado por aprendizaje automático para la causalidad	Método de Control Sintético (SCM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2018-2020	2010
Autor original	Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)	Abadie, Diamond & Hainmueller
Tipo	Sensitivity analysis / causal robustness assessment	Counterfactual causal-inference model
Fuente seminal	Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Alias	ML-augmented sensitivity analysis, ML sensitivity analysis for causality, machine learning sensitivity analysis, debiased ML sensitivity analysis	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)
Relacionados	5	5
Resumen	Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.	The Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.