Federated Causal Discovery From Interventions

TL;DR

FedCDI is a federated causal discovery framework that infers causal structures from distributed interventional data while enhancing privacy and handling data heterogeneity.

Contribution

It introduces a novel federated approach for causal discovery from interventional data, including intervention-aware aggregation and heterogeneity mitigation.

Findings

Effective in synthetic and real-world scenarios

Improves privacy by exchanging belief updates

Handles heterogeneity in interventional data

Abstract

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.