Differentially Private Estimation of Heterogeneous Causal Effects
- Fengshi Niu ,
- Harsha Nori ,
- Brian Quistorff ,
- Rich Caruana ,
- Donald Ngwe ,
- Aadharsh Kannan
Published by First Conference on Causal Learning and Reasoning
![Chart Showing Model Performance](https://www.microsoft.com/en-us/research/uploads/prod/2022/02/CLeaR2022-1024x576.png)
Estimating heterogeneous treatment effects in domains such as healthcare or social science often involves sensitive data where protecting privacy is important. We introduce a general meta-algorithm for estimating conditional average treatment effects (CATE) with differential privacy guarantees. Our meta-algorithm can work with simple, single-stage CATE estimators such as S-learner and more complex multi-stage estimators such as DR and R-learner. We perform a tight privacy analysis by taking advantage of sample splitting in our meta-algorithm and the parallel composition property of differential privacy. In this paper, we implement our approach using DP-EBMs as the base learner. DP-EBMs are interpretable, high-accuracy models with privacy guarantees, which allow us to directly observe the impact of DP noise on the learned causal model. Our experiments show that multi-stage CATE estimators incur larger accuracy loss than single-stage CATE or ATE estimators and that most of the accuracy loss from differential privacy is due to an increase in variance, not biased estimates of treatment effects.