Causaltoolbox—Estimator Stability for Heterogeneous Treatment Effects

Estimating heterogeneous treatment effects has become extremely important in many fields and often life changing decisions for individuals are based on these estimates, for example choosing a medical treatment for a patient. In the recent years, a variety of techniques for estimating heterogeneous treatment effects, each making subtly different assumptions, have been suggested. 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 just one estimator. This approach risks making inferences based on incorrect assumptions and gives the experimenter too much scope for p-hacking. A single estimator will also tend to overlook patterns other estimators would 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 32 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 apply our analysis to their data.