Federated Causal Discovery From Interventions
This work addresses privacy-preserving causal discovery for domains like healthcare where data is distributed, representing an incremental advance by extending federated methods to include interventional data.
The paper tackles the problem of causal discovery from distributed interventional data, proposing FedCDI, a federated framework that exchanges belief updates instead of raw samples to improve privacy and introduces an intervention-aware aggregation method, achieving competitive performance across synthetic and real-world graphs.
Causal discovery serves a pivotal role in mitigating model uncertainty through recovering the underlying causal mechanisms among variables. In many practical domains, such as healthcare, access to the data gathered by individual entities is limited, primarily for privacy and regulatory constraints. However, the majority of existing causal discovery methods require the data to be available in a centralized location. In response, researchers have introduced federated causal discovery. While previous federated methods consider distributed observational data, the integration of interventional data remains largely unexplored. We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples. In line with the federated learning framework, FedCDI improves privacy by exchanging belief updates rather than raw samples. Additionally, it introduces a novel intervention-aware method for aggregating individual updates. We analyze scenarios with shared or disjoint intervened covariates, and mitigate the adverse effects of interventional data heterogeneity. The performance and scalability of FedCDI is rigorously tested across a variety of synthetic and real-world graphs.