Is merging worth it? Securely evaluating the information gain for causal dataset acquisition

TL;DR

This paper introduces a cryptographically secure, privacy-preserving method to evaluate the potential benefits of merging datasets for causal effect estimation without revealing sensitive data, improving decision-making in data integration.

Contribution

It presents the first secure, information-theoretic approach for assessing dataset merge value in causal inference, combining multi-party computation with differential privacy.

Findings

Effective in simulated benchmarks

Reliable in realistic scenarios

Preserves privacy while maintaining accuracy

Abstract

Merging datasets across institutions is a lengthy and costly procedure, especially when it involves private information. Data hosts may therefore want to prospectively gauge which datasets are most beneficial to merge with, without revealing sensitive information. For causal estimation this is particularly challenging as the value of a merge depends not only on reduction in epistemic uncertainty but also on improvement in overlap. To address this challenge, we introduce the first cryptographically secure information-theoretic approach for quantifying the value of a merge in the context of heterogeneous treatment effect estimation. We do this by evaluating the Expected Information Gain (EIG) using multi-party computation to ensure that no raw data is revealed. We further demonstrate that our approach can be combined with differential privacy (DP) to meet arbitrary privacy requirements whilst preserving more accurate computation compared to DP alone. To the best of our knowledge, this work presents the first privacy-preserving method for dataset acquisition tailored to causal estimation. We demonstrate the effectiveness and reliability of our method on a range of simulated and realistic benchmarks. Code is publicly available: https://github.com/LucileTerminassian/causal_prospective_merge.