Causal Discovery in the Era of Agents

2026-06-22

AI Summary

This work addresses the tendency of existing large language models to conflate textual associations or hallucinations with genuine causal evidence when applied to causal discovery, often lacking explicit grounding in data and assumptions. To remedy this, the authors propose a novel paradigm wherein AI agents assist—but do not autonomously generate—causal conclusions. This framework orchestrates data analysis, preprocessing, method recommendation, expert knowledge integration, and result interpretation to ensure that all causal inferences are rigorously based on empirical data, explicit assumptions, and formal algorithms. Built upon the causal-learn ecosystem, the team developed an online platform (causallearn.com) featuring agent-driven capabilities for data validation, context-aware retrieval, assumption articulation, and graphical model explanation. The efficacy and reliability of this human–AI collaborative approach are demonstrated through a case study on Big Five personality data.

Abstract

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.

Problem

• causal discovery
• large language models
• causal evidence
• hallucination
• agent-assisted analysis

Innovation

• causal discovery
• large language models
• agent-assisted workflow
• causal-learn+
• formal causal inference