# Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding

arXiv: https://arxiv.org/abs/2603.05149 · HTML: https://mlsanity.com/paper/2603.05149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Measuring Data Leakage in Machine-Learning Models with Fisher Information](https://mlsanity.com/paper/2102.11673/) (2021)
- [FedDAG: Federated DAG Structure Learning](https://mlsanity.com/paper/2112.03555/) (2021)
- [Federated Causal Discovery from Heterogeneous Data](https://mlsanity.com/paper/2402.13241/) (2024)
- [Towards Practical Federated Causal Structure Learning](https://mlsanity.com/paper/2306.09433/) (2023)
- [Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants](https://mlsanity.com/paper/2507.06888/) (2025)
- [FED-chi2: Privacy Preserving Federated Correlation Test](https://mlsanity.com/paper/2105.14618/) (2021)
- [Fisher Scoring for crossed factor Linear Mixed Models](https://mlsanity.com/paper/2102.05103/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Survey on Federated Causal Discovery and Inference](https://mlsanity.com/paper/2606.23741/) (2026)
- [When Do Causal World Models Help Modular LLM Agents](https://mlsanity.com/paper/2610.00012/) (2026)

## Abstract (arXiv)

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.