Private Causal Inference
This addresses the need for privacy-preserving methods in sensitive applications like medical research and personal finance, though it is incremental as it adapts existing ANM frameworks with privacy enhancements.
The paper tackles the problem of performing causal inference while preserving data privacy, specifically using the additive noise model (ANM) framework with differential privacy guarantees, and demonstrates through experiments that the techniques are practical and easy to implement.
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.