Differentially Private Covariate Balancing Causal Inference
This addresses privacy-sensitive causal inference for researchers handling sensitive datasets, though it is incremental as it builds on existing differential privacy and covariate balancing methods.
The paper tackled the challenge of performing causal inference from observational data while ensuring differential privacy, by developing a two-stage covariate balancing weighting estimator that provides point and interval estimators with statistical guarantees like consistency and rate optimality under a privacy budget.
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.