Adèle Helena Ribeiro

Tiago da Silva, Bruna Bazaluk, Eliezer de Souza da Silva et al.

Causal discovery (CD) algorithms are notably brittle when data is scarce, inferring unreliable causal relations that may contradict expert knowledge, especially when considering latent confounders. Furthermore, the lack of uncertainty quantification in most CD methods hinders users from diagnosing and refining results. To address these issues, we introduce Ancestral GFlowNets (AGFNs). AGFN samples ancestral graphs (AGs) proportionally to a score-based belief distribution representing our epistemic uncertainty over the causal relationships. Building upon this distribution, we propose an elicitation framework for expert-driven assessment. This framework comprises an optimal experimental design to probe the expert and a scheme to incorporate the obtained feedback into AGFN. Our experiments show that: i) AGFN is competitive against other methods that address latent confounding on both synthetic and real-world datasets; and ii) our design for incorporating feedback from a (simulated) human expert or a Large Language Model (LLM) improves inference quality.

Maximilian Hahn, Alina Zajak, Dominik Heider et al.

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.