Differentially private learners for heterogeneous treatment effects
Maresa Schröder Valentyn Melnychuk Stefan Feuerriegel
Patient data is widely used to estimate heterogeneous treatment effects and understand the effectiveness and safety of drugs. Yet, patient data includes highly
sensitive information that must be kept private. In this work, we aim to estimate
the conditional average treatment effect (CATE) from observational data under
differential privacy. Specifically, we present DP-CATE, a novel framework for
CATE estimation that is *Neyman-orthogonal* and ensures *differential privacy* of the estimates.
 Our framework is highly general: it applies to any two-stage
CATE meta-learner with a Neyman-orthogonal loss function and any machine
learning model can be used for nuisance estimation. We further provide an extension of our DP-CATE, where we employ RKHS regression to release the complete
CATE function while ensuring differential privacy. We demonstrate the effectiveness of DP-CATE across various experiments using synthetic and real-world
datasets. To the best of our knowledge, we are the first to provide a framework for
CATE estimation that is doubly robust and differentially private.
Causality, differential privacy, treatment effect estimation