Private Causal Inference

TL;DR

This paper develops differentially private causal inference methods within the additive noise model framework, enabling privacy-preserving causal analysis with practical algorithms and broad applications.

Contribution

It introduces a novel framework that ensures differential privacy in causal inference using ANM, addressing privacy concerns in sensitive data analysis.

Findings

The proposed methods provide strong privacy guarantees.

Experiments show the techniques are practical and easy to implement.

The framework is applicable to various ANM variants.

Abstract

Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference. Causal inference has the potential to have significant impact on medical research, prevention and control of diseases, and identifying factors that impact economic changes to name just a few. However, these promising applications for causal inference are often ones that involve sensitive or personal data of users that need to be kept private (e.g., medical records, personal finances, etc). Therefore, there is a need for the development of causal inference methods that preserve data privacy. We study the problem of inferring causality using the current, popular causal inference framework, the additive noise model (ANM) while simultaneously ensuring privacy of the users. Our framework provides differential privacy guarantees for a variety of ANM variants. We run extensive experiments, and demonstrate that our techniques are practical and easy to implement.