Differentially Private Average Treatment Effect Estimation by Propensity Score Blocking

2026-09-08

AI Summary

本文提出两种基于倾向评分的算法，用于在观察研究中估计平均处理效应，同时保护数据隐私，减少了误差和偏差。

Abstract

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.

Problem

• Differentially Private
• Average Treatment Effect
• Observational Studies
• Propensity Score

Innovation

• Differentially Private
• Average Treatment Effect
• Propensity Score
• Inverse Probability Weighting
• Blocking on the Propensity Score