Expert-Aided Causal Discovery of Ancestral Graphs

• URL: http://arxiv.org/abs/2309.12032v3
• Date: Fri, 10 Oct 2025 19:53:40 GMT
• Title: Expert-Aided Causal Discovery of Ancestral Graphs
• Authors: Tiago da Silva, Bruna Bazaluk, Eliezer de Souza da Silva, António Góis, Dominik Heider, Samuel Kaski, Diego Mesquita, Adèle Helena Ribeiro,
• Abstract summary: Causal discovery algorithms are notably brittle when data is scarce.<n>The lack of uncertainty in most CD methods hinders users from diagnosing and refining results.<n>We introduce Ancestral GFlowNets (AGFNs) to address these issues.
• Score: 25.044662541801618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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