  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.
