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CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference

Jiawei Zhu, Wei Chen, Ruichu Cai

TL;DR

CausalAgent tackles the barrier to broad adoption of causal inference by delivering an end-to-end, conversational MAS that automates data cleaning, causal structure learning, bias correction, and report generation. It integrates retrieval-augmented generation (RAG) and the Model Context Protocol (MCP) to ground reasoning in theory while maintaining interactive, natural-language interaction. Key contributions include explicit workflow modeling, robust data-quality checks with cycle detection, algorithm scheduling that leverages PC and latent-confounder handling methods, and RAG-backed reporting with interactive visualizations for exploration and interpretation. Demonstration on the Sachs dataset illustrates practical utility, highlighting master regulators Akt and Pka and enabling scenario analyses through interactive refinement, signaling meaningful real-world impact and interpretability.

Abstract

Causal inference holds immense value in fields such as healthcare, economics, and social sciences. However, traditional causal analysis workflows impose significant technical barriers, requiring researchers to possess dual backgrounds in statistics and computer science, while manually selecting algorithms, handling data quality issues, and interpreting complex results. To address these challenges, we propose CausalAgent, a conversational multi-agent system for end-to-end causal inference. The system innovatively integrates Multi-Agent Systems (MAS), Retrieval-Augmented Generation (RAG), and the Model Context Protocol (MCP) to achieve automation from data cleaning and causal structure learning to bias correction and report generation through natural language interaction. Users need only upload a dataset and pose questions in natural language to receive a rigorous, interactive analysis report. As a novel user-centered human-AI collaboration paradigm, CausalAgent explicitly models the analysis workflow. By leveraging interactive visualizations, it significantly lowers the barrier to entry for causal analysis while ensuring the rigor and interpretability of the process.

CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference

TL;DR

CausalAgent tackles the barrier to broad adoption of causal inference by delivering an end-to-end, conversational MAS that automates data cleaning, causal structure learning, bias correction, and report generation. It integrates retrieval-augmented generation (RAG) and the Model Context Protocol (MCP) to ground reasoning in theory while maintaining interactive, natural-language interaction. Key contributions include explicit workflow modeling, robust data-quality checks with cycle detection, algorithm scheduling that leverages PC and latent-confounder handling methods, and RAG-backed reporting with interactive visualizations for exploration and interpretation. Demonstration on the Sachs dataset illustrates practical utility, highlighting master regulators Akt and Pka and enabling scenario analyses through interactive refinement, signaling meaningful real-world impact and interpretability.

Abstract

Causal inference holds immense value in fields such as healthcare, economics, and social sciences. However, traditional causal analysis workflows impose significant technical barriers, requiring researchers to possess dual backgrounds in statistics and computer science, while manually selecting algorithms, handling data quality issues, and interpreting complex results. To address these challenges, we propose CausalAgent, a conversational multi-agent system for end-to-end causal inference. The system innovatively integrates Multi-Agent Systems (MAS), Retrieval-Augmented Generation (RAG), and the Model Context Protocol (MCP) to achieve automation from data cleaning and causal structure learning to bias correction and report generation through natural language interaction. Users need only upload a dataset and pose questions in natural language to receive a rigorous, interactive analysis report. As a novel user-centered human-AI collaboration paradigm, CausalAgent explicitly models the analysis workflow. By leveraging interactive visualizations, it significantly lowers the barrier to entry for causal analysis while ensuring the rigor and interpretability of the process.
Paper Structure (10 sections, 1 figure)

This paper contains 10 sections, 1 figure.

Table of Contents

  1. Introduction
  2. System Overview
  3. Architecture
  4. Data Processing Agent
  5. Causal Structure Learning Agent
  6. Reporting Agent
  7. Key Implementation
  8. Demonstration Scenario
  9. Workflow Walkthrough
  10. Conclusion and Future Work

Figures (1)

  • Figure 1: Demonstration of CausalAgent on the Sachs Dataset. (A) CausalAgent Running process. (B) Correlation heatmap of variables. (C) Generated Causal diagram.