  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.
