Differentially private estimation of weighted average treatment effects for binary outcomes

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, new algorithms are developed for estimating weighted average treatment effects with binary outcomes that satisfy the criterion of differential privacy. Theoretical results are presented on the accuracy of several differentially private estimators of weighted average treatment effects. Empirical evaluations using simulated data and a causal analysis involving education and income data illustrate the performance of these estimators.