Differentially Private Multi-Site Treatment Effect Estimation
Tatsuki Koga Kamalika Chaudhuri David Page
Patient privacy is a major barrier to healthcare AI. For confidentiality
reasons, most patient data remains in silo in separate hospitals, preventing
the design of data-driven healthcare AI systems that need large volumes of
patient data to make effective decisions. A solution to this is collective
learning across multiple sites through federated learning with differential
privacy. However, literature in this space typically focuses on differentially
private statistical estimation and machine learning, which is different from
the causal inference-related problems that arise in healthcare. In this work,
we take a fresh look at federated learning with a focus on causal inference;
specifically, we look at estimating the average treatment effect (ATE), an
important task in causal inference for healthcare applications, and provide a
federated analytics approach to enable ATE estimation across multiple sites
along with differential privacy (DP) guarantees at each site. The main
challenge comes from site heterogeneity -- different sites have different
sample sizes and privacy budgets. We address this through a class of per-site
estimation algorithms that reports the ATE estimate and its variance as a
quality measure, and an aggregation algorithm on the server side that minimizes
the overall variance of the final ATE estimate. Our experiments on real and
synthetic data show that our method reliably aggregates private statistics
across sites and provides better privacy-utility tradeoff under site
heterogeneity than baselines.