@inproceedings{niu2022differentially,
author = {Niu, Fengshi and Nori, Harsha and Quistorff, Brian and Caruana, Rich and Ngwe, Donald and Kannan, Aadharsh},
title = {Differentially Private Estimation of Heterogeneous Causal Effects},
booktitle = {CLeaR 2022},
year = {2022},
month = {April},
abstract = {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.},
publisher = {First Conference on Causal Learning and Reasoning},
url = {http://approjects.co.za/?big=en-us/research/publication/differentially-private-estimation-of-heterogeneous-causal-effects/},
}