Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Double Machine Learning and Automated Confounder Selection -- A
  Cautionary Tale   Double machine learning (DML) is becoming an increasingly popular tool for
automatic model selection in high-dimensional settings. These approaches rely
on the assumption of conditional independence, which may not hold in big-data
settings where the covariate space is large. This paper shows that DML is very
sensitive to the inclusion of even a few "bad controls" in the covariate space.
The resulting bias varies with the nature of the causal model, which raises
concerns about the feasibility of selecting control variables in a data-driven
way.

11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
1
The categories of this paper are: cs
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio