Data-Driven Model Selection and Causal Effects in Regression Models
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.
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
Kontakt: Konstantin Görgen