Causal Inference with Differentially Private (Clustered) Outcomes. (arXiv:2308.00957v1 [stat.ML])

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 makes the precise measurement of …

algorithm de-anonymization differential privacy guarantee measure outcomes privacy private risk running share