Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding

TL;DR

This paper introduces fedCI and fedCI-IOD, innovative federated methods for causal discovery across heterogeneous datasets that preserve privacy, handle diverse variable types, and outperform traditional approaches in statistical power.

Contribution

The paper presents the first federated causal discovery method capable of handling heterogeneous datasets with latent confounding and various variable types, advancing privacy-preserving causal analysis.

Findings

fedCI effectively handles diverse variable types and heterogeneity.

fedCI-IOD enables federated causal discovery under latent confounding.

Methods achieve performance comparable to pooled analyses.

Abstract

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.