  Estimating heterogeneous treatment effects has become increasingly important
in many fields and life and death decisions are now based on these estimates:
for example, selecting a personalized course of medical treatment. Recently, a
variety of procedures relying on different assumptions have been suggested for
estimating heterogeneous treatment effects. Unfortunately, there are no
compelling approaches that allow identification of the procedure that has
assumptions that hew closest to the process generating the data set under study
and researchers often select one arbitrarily. This approach risks making
inferences that rely on incorrect assumptions and gives the experimenter too
much scope for $p$-hacking. A single estimator will also tend to overlook
patterns other estimators could have picked up. We believe that the conclusion
of many published papers might change had a different estimator been chosen and
we suggest that practitioners should evaluate many estimators and assess their
similarity when investigating heterogeneous treatment effects. We demonstrate
this by applying 28 different estimation procedures to an emulated
observational data set; this analysis shows that different estimation
procedures may give starkly different estimates. We also provide an extensible
\texttt{R} package which makes it straightforward for practitioners to follow
our recommendations.
