Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomes

TL;DR

This paper develops differentially private algorithms for estimating weighted average treatment effects with binary outcomes, balancing data confidentiality with causal inference accuracy in sensitive social science data.

Contribution

It introduces novel differentially private estimators for causal effects with binary outcomes and provides theoretical and empirical evaluations of their performance.

Findings

Estimators satisfy differential privacy guarantees.

Theoretical bounds on estimation accuracy.

Empirical validation with simulated and real data.

Abstract

In the social and health sciences, researchers often make causal inferences using sensitive variables. These researchers, as well as the data holders themselves, may be ethically and perhaps legally obligated to protect the confidentiality of study participants' data. It is now known that releasing any statistics, including estimates of causal effects, computed with confidential data leaks information about the underlying data values. Thus, analysts may desire to use causal estimators that can provably bound this information leakage. Motivated by this goal, we develop algorithms for estimating weighted average treatment effects with binary outcomes that satisfy the criterion of differential privacy. We present theoretical results on the accuracy of several differentially private estimators of weighted average treatment effects. We illustrate the empirical performance of these estimators using simulated data and a causal analysis using data on education and income.