Causal Inference with Differentially Private (Clustered) Outcomes

arXiv:2308.00957v2 Announce Type: replace-cross
Abstract: Estimating causal effects from randomized experiments is only feasible if participants agree to reveal their potentially sensitive responses. Of the many ways of ensuring privacy, label differential privacy is a widely used measure of an algorithm's privacy guarantee, which might encourage participants to share responses without running the risk of de-anonymization. Many differentially private mechanisms inject noise into the original data-set to achieve this privacy guarantee, which increases the variance of most statistical estimators and …

algorithm arxiv cs.cr cs.lg differential privacy guarantee measure outcomes privacy private reveal running sensitive share stat.me stat.ml