Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking
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Abstract (arXiv)
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.
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References · 9
- 2022Differentially Private Estimation of Heterogeneous Causal Effectscited by 14
- 2023Multiply Robust Federated Estimation of Targeted Average Treatment Effectscited by 13
- 2019Privacy-Preserving Causal Inference via Inverse Probability Weightingcited by 9
- 2024Differentially Private Estimation of Weighted Average Treatment Effects for Binary Outcomescited by 6
- 2024Differentially Private Covariate Balancing Causal Inferencecited by 5
- 2025Differentially Private Learners for Heterogeneous Treatment Effectscited by 5
- 2023Differentially Private Multi-Site Treatment Effect Estimationcited by 4
- 2025Model Agnostic Differentially Private Causal Inferencecited by 2
- 2025PrivATE: Differentially Private Confidence Intervals for Average Treatment Effectscited by 2
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