Abstract

The development of accurate causal models is crucial for achieving and explaining desired outcomes that require interventions. Building these models efficiently requires combining available data with expert causal knowledge. Often experts have unique data and model insights, but sharing them is challenging due to privacy or security concerns. Federated machine learning addresses similar issues by allowing multiple sites to collaborate on a common model without sharing private datasets. This paper introduces CCaT, a distributed causal discovery tool enabling collaborative development of a shared causal model while preserving local models and data privacy. CCaT allows each site to evaluate and refine the shared model using its private dataset, sharing only summary statistics or suggested new causal relations. The tool supports maintaining distinct local causal models, as analysts can choose to adopt or …

Date

September 14, 2024

Authors

Alexey Tregubov, Jeremy Abramson, Stephen Schwab, Jim Blythe

Book

International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation

Pages

100-109

Publisher

Springer Nature Switzerland