Privacy-Preserving Causal Inference via Inverse Probability Weighting
Machine Learning
2019-11-04 v2 Machine Learning
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.
Keywords
Cite
@article{arxiv.1905.12592,
title = {Privacy-Preserving Causal Inference via Inverse Probability Weighting},
author = {Si Kai Lee and Luigi Gresele and Mijung Park and Krikamol Muandet},
journal= {arXiv preprint arXiv:1905.12592},
year = {2019}
}