Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semiparametric Difference-in-Differences with Potentially Many Control
  Variables   This paper discusses difference-in-differences (DID) estimation when there
exist many control variables, potentially more than the sample size. In this
case, traditional estimation methods, which require a limited number of
variables, do not work. One may consider using statistical or machine learning
(ML) methods. However, by the well-known theory of inference of ML methods
proposed in Chernozhukov et al. (2018), directly applying ML methods to the
conventional semiparametric DID estimators will cause significant bias and make
these DID estimators fail to be sqrt{N}-consistent. This article proposes three
new DID estimators for three different data structures, which are able to
shrink the bias and achieve sqrt{N}-consistency and asymptotic normality with
mean zero when applying ML methods. This leads to straightforward inferential
procedures. In addition, I show that these new estimators have the small bias
property (SBP), meaning that their bias will converge to zero faster than the
pointwise bias of the nonparametric estimator on which it is based.

3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
1
The categories of this paper are: cs
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas