  There is strong interest in estimating how the magnitude of treatment effects
of an intervention vary across sub-groups of the population of interest. In our
paper, we propose a two-study approach to first propose and then test
heterogeneous treatment effects. In Study 1, we use a large observational
dataset to learn sub-groups with the most distinctive treatment-outcome
relationships ('high/low-impact sub-groups'). We adopt a model-based recursive
partitioning approach to propose the high/low impact sub-groups, and validate
them by using sample-splitting. While the first study rules out noise, there is
potential bias in our estimated heterogeneous treatment effects. Study 2 uses
an experimental design, and here we classify our sample units based on
sub-groups learned in Study 1. We then estimate treatment effects within each
of the groups, thereby testing the causal hypotheses proposed in Study 1. Using
patient claims data from the NBER MarketScan database, we apply our approach to
estimate heterogeneous effects of a switch to a high-deductible health
insurance plan on use of outpatient care by patients with a common chronic
condition. We extend the method to non-parametrically learn the sub-groups in
Study 1. We also compare the methods' performance to other state-of-the-art
methods in the literature that make use only of the Study 2 data.
