Privacy-Preserving Causal Inference via Inverse Probability Weighting

TL;DR

This paper introduces a new framework for privacy-preserving inverse probability weighting (PP-IPW) methods, enabling causal effect estimation from sensitive observational data while maintaining privacy, supported by theoretical analysis and empirical validation.

Contribution

It proposes the first privacy-preserving IPW framework, combining privacy guarantees with causal inference, and provides theoretical and empirical evaluation of its effectiveness.

Findings

Theoretical analysis shows the privacy mechanism impacts the estimated effects.

Empirical results validate the theoretical predictions across datasets.

PP-IPW maintains privacy without significantly compromising causal estimates.

Abstract

The use of inverse probability weighting (IPW) methods to estimate the causal effect of treatments from observational studies is widespread in econometrics, medicine and social sciences. Although these studies often involve sensitive information, thus far there has been no work on privacy-preserving IPW methods. We address this by providing a novel framework for privacy-preserving IPW (PP-IPW) methods. We include a theoretical analysis of the effects of our proposed privatisation procedure on the estimated average treatment effect, and evaluate our PP-IPW framework on synthetic, semi-synthetic and real datasets. The empirical results are consistent with our theoretical findings.