Differentially Private Covariate Balancing Causal Inference

• URL: http://arxiv.org/abs/2410.14789v1
• Date: Fri, 18 Oct 2024 18:02:13 GMT
• Title: Differentially Private Covariate Balancing Causal Inference
• Authors: Yuki Ohnishi, Jordan Awan,
• Abstract summary: Differential privacy is the leading mathematical framework for privacy protection. Our algorithm produces both point and interval estimators with statistical guarantees, such as consistency and rate optimality, under a given privacy budget.
• Score: 8.133739801185271
• License:
• 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.

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