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 of a global causal structure unachievable by any individual agent alone. We advocate for a new research frontier and outline a comprehensive framework for new research thrusts that range from eliciting, modeling, aggregating, and optimizing human causal knowledge contributions.

翻译：从观测数据中学习通常以有向无环图（DAG）表示的因果结构，由于可能图结构的组合爆炸以及观测中固有的模糊性，历来极具挑战性。本文认为，在快速发展的技术支持下，因果学习现已准备好迎接一个新范式的出现，以实现利用人类因果知识的长期愿景。该范式整合了用于数据收集的可扩展众包平台、用于专家意见建模的交互式知识启发、用于专家意见协调的稳健聚合技术，以及基于大型语言模型（LLM）的仿真以增强人工智能驱动的信息获取。在本文中，我们聚焦于用于因果发现的DAG学习，并将该问题构建为一个分布式决策任务，认识到每个参与者（人类专家或LLM智能体）仅拥有关于因果图中感兴趣变量的不同子集的、碎片化且不完美的知识。通过提出一个系统性的框架来综合这些洞见，我们的目标是能够恢复任何单个智能体都无法独自获得的全局因果结构。我们倡导一个新的研究前沿，并勾勒出一个全面的框架，涵盖从启发、建模、聚合到优化人类因果知识贡献的一系列新的研究重点。