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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.