  This paper conducts a methodological review of papers using the causal forest
machine learning method for flexibly estimating heterogeneous treatment
effects. It examines 133 peer-reviewed papers. It shows that the emerging best
practice relies heavily on the approach and tools created by the original
authors of the causal forest such as their grf package and the approaches given
by them in examples. Generally researchers use the causal forest on a
relatively low-dimensional dataset relying on randomisation or observed
controls to identify effects. There are several common ways to then communicate
results -- by mapping out the univariate distribution of individual-level
treatment effect estimates, displaying variable importance results for the
forest and graphing the distribution of treatment effects across covariates
that are important either for theoretical reasons or because they have high
variable importance. Some deviations from this common practice are interesting
and deserve further development and use. Others are unnecessary or even
harmful.
