Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking
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 t...
| Authors | Duncan Stewardson, Grayson W. White, Adam Groce |
|---|---|
| Primary category | stat.ME |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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