Comparer des méthodes

	Variables instrumentales augmentées par l'apprentissage automatique (ML-IV)	Régression Lasso
Domaine	Inférence causale	Apprentissage automatique
Famille	Regression model	Machine learning
Année d'origine	2012-2018	1996
Auteur d'origine	Belloni, Chernozhukov & Hansen; Chernozhukov et al.	Tibshirani, R.
Type	Causal inference / semi-parametric estimation	Regularized linear regression (L1 penalty)
Source fondatrice	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.	Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Alias	ML-IV, MLIV, Double/Debiased ML with IV, DML-IV	LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization
Apparentées	4	4
Résumé	Machine learning-augmented instrumental variables combines the causal identification power of classical IV with modern high-dimensional machine learning — using methods such as LASSO, random forests, or neural networks to select valid instruments and model nuisance functions, thereby improving first-stage fit and enabling valid inference even when the number of potential instruments or controls is large relative to the sample size.	Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter.