智能體時代下的因果發現

摘要

近期，將大型語言模型（LLMs）與因果發現結合的嘗試，要求模型推斷成對方向、提出圖結構，或將語言模型輸出注入作為先驗知識與約束條件。這些方法雖有望加速分析，卻也模糊了因果證據究竟是來自資料與假設，還是源自文本關聯、提示偽影及幻覺機制。我們主張代理人（agents）在因果發現中應扮演不同的角色。代理人應檢視資料、檢索背景脈絡、解釋方法假設，並釐清圖形輸出，但不應提供邊線、方向、先驗知識、約束條件或因果結論。我們提出一項原則：代理人協助工作流程，而因果主張仍須立基於資料、明確假設、正規演算法、診斷工具，以及使用者或領域專家的決策。我們將此原則具體落實於 causal-learn+ 這個線上平台，該平台圍繞 causal-learn 的演算法生態系統，協調資料分析、前處理、方法推薦、專家知識整合、正規發現與解讀。一項以「大五人格」資料為案例的研究，展示了在因果發現中，如何透過代理人輔助流程，避免將語言模型的不確定性轉化為因果證據。該平台可於 causallearn.com 使用。

English

Recent attempts to combine large language models (LLMs) with causal discovery ask models to infer pairwise directions, propose graph structures, or inject language-model outputs as priors and constraints. These approaches promise faster analysis, but they also obscure whether a causal evidence is supported by data and assumptions or by textual associations, prompt artifacts and hallucinated mechanisms. We argue for a different role for agents in causal discovery. Agents should inspect data, retrieve context, explain method assumptions and clarify graph outputs, but they should not supply edges, orientations, priors, constraints or causal conclusions. We propose the principle that agents assist the workflow, while causal claims remain grounded in data, explicit assumptions, formal algorithms, diagnostics and user or domain-expert decisions. We instantiate this principle in causal-learn+, an online platform that coordinates data analysis, preprocessing, method recommendation, expert-knowledge incorporation, formal discovery and interpretation around the algorithmic ecosystem of causal-learn. A case study on Big Five personality data illustrates agent-assisted pipeline of causal discovery without turning language-model unreliability into causal evidence. The platform is available at causallearn.com.