  Estimating heterogeneous treatment effects in domains such as healthcare or
social science often involves sensitive data where protecting privacy is
important. We introduce a general meta-algorithm for estimating conditional
average treatment effects (CATE) with differential privacy (DP) guarantees. Our
meta-algorithm can work with simple, single-stage CATE estimators such as
S-learner and more complex multi-stage estimators such as DR and R-learner. We
perform a tight privacy analysis by taking advantage of sample splitting in our
meta-algorithm and the parallel composition property of differential privacy.
In this paper, we implement our approach using DP-EBMs as the base learner.
DP-EBMs are interpretable, high-accuracy models with privacy guarantees, which
allow us to directly observe the impact of DP noise on the learned causal
model. Our experiments show that multi-stage CATE estimators incur larger
accuracy loss than single-stage CATE or ATE estimators and that most of the
accuracy loss from differential privacy is due to an increase in variance, not
biased estimates of treatment effects.
