Differentially Private Covariate Balancing Causal Inference

TL;DR

This paper introduces a differentially private covariate balancing method for causal inference that ensures privacy while providing statistically valid causal effect estimates from observational data.

Contribution

It proposes a novel two-stage covariate balancing estimator that maintains privacy and achieves consistency and optimality under differential privacy constraints.

Findings

Provides point and interval estimators with statistical guarantees.

Ensures privacy-preserving covariate balance in causal inference.

Achieves rate optimality under a specified privacy budget.

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.