Identification and estimation of causal effects in statistical models is important for interpretation of the model. When the number of covariates is high, however, standard OLS methods often fail. Data-driven variable selection methods such as the Lasso can solve such problems, with a wide range of applications.

Literature:

Tibshirani, R. (1996): Regression Selection and Shrinkage via the Lasso," Journal of the Royal Statistical Society: Series B (Methodological), 58, 267-288.

Zou, H. and T. Hastie (2005): Regression and variable selection via the elastic net,"
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 301-320.

Belloni, A., V. Chernozhukov, and C. Hansen (2014): High-Dimensional Methods
and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, 28, 29-50.

Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins, Double/debiased machine learning for treatment and structural parameters, The Econometrics Journal, Volume 21, Issue 1, 1 February 2018, Pages C1–C68, https://doi.org/10.1111/ectj.12097

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