Comparar métodos

	Estimador de Concordancia Aumentado por Aprendizaje Automático	Estimación Doblemente Robusta Aumentada por Aprendizaje Automático (ML-DR)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2006–2018	2018
Autor original	Abadie & Imbens (classical matching); Chernozhukov et al. (ML augmentation framework)	Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey & Robins
Tipo	Causal inference / nonparametric matching	Semiparametric causal estimator with ML nuisance
Fuente 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.	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.
Alias	ML-augmented matching, ML matching estimator, high-dimensional matching estimator, data-adaptive matching estimator	ML-DR, AIPW with ML, Double/Debiased ML doubly robust, DML-DR
Relacionados	5	6
Resumen	The machine learning-augmented matching estimator combines classical nearest-neighbor or propensity-score matching with ML algorithms — such as lasso, random forests, or gradient boosting — to select covariates, estimate propensity scores, and correct for residual bias. The result is a matching-based causal estimator that remains valid under high-dimensional confounding where traditional hand-specified matching fails.	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.