Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit
  Performance   Estimation of causal effects using machine learning methods has become an
active research field in econometrics. In this paper, we study the finite
sample performance of meta-learners for estimation of heterogeneous treatment
effects under the usage of sample-splitting and cross-fitting to reduce the
overfitting bias. In both synthetic and semi-synthetic simulations we find that
the performance of the meta-learners in finite samples greatly depends on the
estimation procedure. The results imply that sample-splitting and cross-fitting
are beneficial in large samples for bias reduction and efficiency of the
meta-learners, respectively, whereas full-sample estimation is preferable in
small samples. Furthermore, we derive practical recommendations for application
of specific meta-learners in empirical studies depending on particular data
characteristics such as treatment shares and sample size.

1
The categories of this paper are: cs
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an