Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding
This work addresses the critical problem of privacy-preserving causal discovery across distributed and heterogeneous datasets, which is crucial for researchers and practitioners in fields with strict data privacy regulations.
This paper introduces fedCI, a federated conditional independence test, and fedCI-IOD, a federated causal discovery algorithm, designed to operate across heterogeneous datasets while preserving privacy. The methods achieve performance comparable to fully pooled analyses, even with low local sample sizes, by federatively aggregating evidence.
Causal discovery across multiple datasets is often constrained by data privacy regulations and cross-site heterogeneity, limiting the use of conventional methods that require a single, centralized dataset. To address these challenges, we introduce fedCI, a federated conditional independence test that rigorously handles heterogeneous datasets with non-identical sets of variables, site-specific effects, and mixed variable types, including continuous, ordinal, binary, and categorical variables. At its core, fedCI uses a federated Iteratively Reweighted Least Squares (IRLS) procedure to estimate the parameters of generalized linear models underlying likelihood-ratio tests for conditional independence. Building on this, we develop fedCI-IOD, a federated extension of the Integration of Overlapping Datasets (IOD) algorithm, that replaces its meta-analysis strategy and enables, for the fist time, federated causal discovery under latent confounding across distributed and heterogeneous datasets. By aggregating evidence federatively, fedCI-IOD not only preserves privacy but also substantially enhances statistical power, achieving performance comparable to fully pooled analyses and mitigating artifacts from low local sample sizes. Our tools are publicly available as the fedCI Python package, a privacy-preserving R implementation of IOD, and a web application for the fedCI-IOD pipeline, providing versatile, user-friendly solutions for federated conditional independence testing and causal discovery.