[2603.02678] Causal Learning Should Embrace the Wisdom of the Crowd

Computer Science > Machine Learning arXiv:2603.02678 (cs) [Submitted on 3 Mar 2026] Title:Causal Learning Should Embrace the Wisdom of the Crowd Authors:Ryan Feng Lin, Yuantao Wei, Huiling Liao, Xiaoning Qian, Shuai Huang View a PDF of the paper titled Causal Learning Should Embrace the Wisdom of the Crowd, by Ryan Feng Lin and 4 other authors View PDF Abstract:Learning causal structures typically represented by directed acyclic graphs (DAGs) from observational data is notoriously challenging due to the combinatorial explosion of possible graphs and inherent ambiguities in observations. This paper argues that causal learning is now ready for the emergence of a new paradigm supported by rapidly advancing technologies, fulfilling the long-standing vision of leveraging human causal knowledge. This paradigm integrates scalable crowdsourcing platforms for data collection, interactive knowledge elicitation for expert opinion modeling, robust aggregation techniques for expert reconciliation, and large language model (LLM)-based simulation for augmenting AI-driven information acquisition. In this paper, we focus on DAG learning for causal discovery and frame the problem as a distributed decision-making task, recognizing that each participant (human expert or LLM agent) possesses fragmented and imperfect knowledge about different subsets of the variables of interest in the causal graph. By proposing a systematic framework to synthesize these insights, we aim to enable the recovery ...