Privacy-Preserving Causal Inference via Inverse Probability Weighting
This addresses privacy concerns in sensitive observational studies for fields like econometrics and medicine, but it is incremental as it adapts an existing method.
The authors tackled the lack of privacy-preserving methods for inverse probability weighting in causal inference, introducing a novel framework (PP-IPW) and showing empirical results consistent with theoretical analysis on synthetic and real datasets.
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.