Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making. However, different causal discovery algorithms can produce divergent results that conflict with each other, complicating the identification of accurate causal graphs. Traditional approaches rely on numerical values and statistical assumptions, often ignoring rich domain-specific information, such as feature descriptions, which could also help structure learning. While rece

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ethics.ai (9 June 2026), “Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting,” evidence record 1210, https://ethics.ai/record/1210 (originally published by arXiv).

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