Mixture-of-Chains: Learning Causal Graphs from Human Knowledge

Human causal knowledge offers a valuable yet underexplored source of structural information for causal discovery. However, eliciting and using such knowledge can be nontrivial, since causal beliefs are typically heterogeneous across individuals and human judgments can be noisy. Existing methods tend to treat human input as auxiliary constraints or priors, failing to capture the underlying causal structure and frequently yielding unreliable or cyclic graphs. In this paper, we propose Mixture-of-Chains (MOC), a framework for directly learning causal graphs from human causal judgments by modeling them as a mixture of latent causal chains. Each chain represents a coherent pathway in human belief structures, allowing MOC to capture variability across reasoning contexts or different individuals. The framework adaptively extracts these chains from noisy inputs and integrates them into a globally consistent causal graph. Experiments on synthetic Bayesian network benchmarks and real human data show that MOC can effectively recover the ground-truth causal structures with strong robustness to input noise and inter-subject variability, supported by the theoretical analysis for recovery conditions. These results suggest that human knowledge can serve not merely as a supplement to data-driven methods, but as a primary source for causal graph learning, and highlight the promise of chain-level modeling for reliable causal discovery.