Differentially Private Learners for Heterogeneous Treatment Effects
This work addresses privacy concerns in healthcare data analysis for estimating treatment effects, though it is incremental as it builds on existing CATE meta-learners with added privacy guarantees.
The authors tackled the problem of estimating heterogeneous treatment effects from sensitive patient data by developing a differentially private framework, DP-CATE, which ensures privacy while maintaining accuracy across synthetic and real-world datasets.
Patient data is widely used to estimate heterogeneous treatment effects and thus 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 further 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 our 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 Neyman-orthogonal and differentially private.