Corpus ID: 237289700

Double Machine Learning and Bad Controls -- A Cautionary Tale

@inproceedings{Hunermund2021DoubleML,
  title={Double Machine Learning and Bad Controls -- A Cautionary Tale},
  author={Paul Hunermund and Beyers Louw and Itamar Caspi},
  year={2021}
}
Double machine learning (DML) is becoming an increasingly popular tool for automated model selection in high-dimensional settings. At its core, DML assumes unconfoundedness, or exogeneity of all considered controls, which might likely be violated if the covariate space is large. In this paper, we lay out a theory of bad controls building on the graph-theoretic approach to causality. We then demonstrate, based on simulation studies and an application to real-world data, that DML is very… Expand

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