  Double machine learning (DML) has become an increasingly popular tool for
automated variable selection in high-dimensional settings. Even though the
ability to deal with a large number of potential covariates can render
selection-on-observables assumptions more plausible, there is at the same time
a growing risk that endogenous variables are included, which would lead to the
violation of conditional independence. This paper demonstrates that DML is very
sensitive to the inclusion of only a few "bad controls" in the covariate space.
The resulting bias varies with the nature of the theoretical causal model,
which raises concerns about the feasibility of selecting control variables in a
data-driven way.
