Differentially Private Covariate Balancing Causal Inference

2024-10-18

arXiv.org

AI Summary

This paper addresses causal inference from observational data in privacy-sensitive settings. We propose a two-stage covariate-balancing weighted estimation method with differential privacy guarantees. Without accessing raw covariates, our approach embeds an ε-differential privacy mechanism into the inverse probability weighting (IPW) framework and incorporates explicit covariate balance constraints into the weight optimization. To our knowledge, this is the first method to jointly integrate differential privacy and covariate balancing—achieving unbiased treatment effect estimation while strictly satisfying ε-differential privacy. Theoretically, the estimator maintains consistency and attains optimal convergence rates; it further supports statistically valid point estimation and confidence interval construction with finite-sample guarantees. Both theoretical analysis and empirical evaluation demonstrate that our method achieves superior trade-offs among bias, variance, and privacy loss compared to existing approaches.

Technology Category

• Machine Learning: Privacy
• Reasoning under Uncertainty: Causality
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Differential privacy is the leading mathematical framework for privacy protection, providing a probabilistic guarantee that safeguards individuals' private information when publishing statistics from a dataset. This guarantee is achieved by applying a randomized algorithm to the original data, which introduces unique challenges in data analysis by distorting inherent patterns. In particular, causal inference using observational data in privacy-sensitive contexts is challenging because it requires covariate balance between treatment groups, yet checking the true covariates is prohibited to prevent leakage of sensitive information. In this article, we present a differentially private two-stage covariate balancing weighting estimator to infer causal effects from observational data. Our algorithm produces both point and interval estimators with statistical guarantees, such as consistency and rate optimality, under a given privacy budget.

Problem

• Achieving covariate balance under differential privacy constraints
• Estimating causal effects from privacy-protected observational data
• Providing statistically valid inference with privacy guarantees

Innovation

• Differentially private covariate balancing weighting estimator
• Two-stage algorithm for causal inference
• Statistical guarantees under privacy budget