PrivATE: Differentially Private Average Treatment Effect Estimation for Observational Data
Quan Yuan
Network and Distributed System Security (NDSS) Symposium 2026 · Day 1 · Privacy & Measurement
Causal inference -- determining whether a treatment or policy actually causes an observed effect -- is fundamental to medicine, economics, and education. When randomized controlled trials are infeasible, researchers rely on observational data that often contains sensitive personal information. This talk introduces **PrivATE**, a framework for estimating **Average Treatment Effects (ATE)** from observational data under **differential privacy (DP)** guarantees. PrivATE offers two levels of privacy protection (label-level and sample-level), uses an **adaptive matching limit** mechanism that automatically adjusts based on privacy budget and data characteristics, and outperforms existing methods across multiple datasets and privacy settings.
AI review
A differential privacy framework for causal inference (average treatment effect estimation) from observational data. Clean statistical work with an adaptive matching mechanism, but this is a privacy/statistics paper with zero security research content. No attacks, no defenses, no vulnerabilities -- purely privacy-preserving data analysis methodology.