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The authors design two differentially private mechanisms for average treatment effect (ATE) estimation in observational settings: an improved inverse probability weighting (IPW) estimator and a novel propensity score blocking (BPS) framework. Valid causal inference on sensitive observational data is frequently hobbled by the extreme sensitivity and variance introduced when injecting differential privacy noise into propensity-weighted statistics. By controlling estimator sensitivity via stratification, the proposed BPS algorithm mitigates estimation bias and reduces ATE error by more than 75% relative to prior private causal inference baselines.
Differentially private causal effect estimation no longer requires sacrificing statistical precision: propensity score blocking cuts estimation error by over 75% compared to state-of-the-art private IPW baselines.
Average treatment effect (ATE) estimation in observational studies is a fundamental statistical tool used frequently in social science, medicine, and other fields. These fields often work with sensitive data where privacy protections are important, so a differentially private mechanism for ATE estimation is highly desirable. Here we present two propensity score-based algorithms for ATE estimation on observational data, one improving the inverse probability weighting (IPW) method used in prior work, and the other using blocking on the propensity score (BPS). Both show lower error and less bias than prior work, with the BPS-based algorithm frequently reducing error by 75% or more compared to prior work.
