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.



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    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,

    Kontakt: Konstantin Görgen