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Traceable Latent Variable Discovery Based on Multi-Agent Collaboration

Huaming Du, Tao Hu, Yijie Huang, Yu Zhao, Guisong Liu, Tao Gu, Gang Kou, Carl Yang

TL;DR

A novel causal modeling framework, TLVD, which integrates the metadata-based reasoning capabilities of large language models (LLMs) with the data-driven modeling capabilities of TCDA for inferring latent variables and their semantics and to validate the inferred latent variables across multiple real-world web-based data sources.

Abstract

Revealing the underlying causal mechanisms in the real world is crucial for scientific and technological progress. Despite notable advances in recent decades, the lack of high-quality data and the reliance of traditional causal discovery algorithms (TCDA) on the assumption of no latent confounders, as well as their tendency to overlook the precise semantics of latent variables, have long been major obstacles to the broader application of causal discovery. To address this issue, we propose a novel causal modeling framework, TLVD, which integrates the metadata-based reasoning capabilities of large language models (LLMs) with the data-driven modeling capabilities of TCDA for inferring latent variables and their semantics. Specifically, we first employ a data-driven approach to construct a causal graph that incorporates latent variables. Then, we employ multi-LLM collaboration for latent variable inference, modeling this process as a game with incomplete information and seeking its Bayesian Nash Equilibrium (BNE) to infer the possible specific latent variables. Finally, to validate the inferred latent variables across multiple real-world web-based data sources, we leverage LLMs for evidence exploration to ensure traceability. We comprehensively evaluate TLVD on three de-identified real patient datasets provided by a hospital and two benchmark datasets. Extensive experimental results confirm the effectiveness and reliability of TLVD, with average improvements of 32.67% in Acc, 62.21% in CAcc, and 26.72% in ECit across the five datasets.

Traceable Latent Variable Discovery Based on Multi-Agent Collaboration

TL;DR

A novel causal modeling framework, TLVD, which integrates the metadata-based reasoning capabilities of large language models (LLMs) with the data-driven modeling capabilities of TCDA for inferring latent variables and their semantics and to validate the inferred latent variables across multiple real-world web-based data sources.

Abstract

Revealing the underlying causal mechanisms in the real world is crucial for scientific and technological progress. Despite notable advances in recent decades, the lack of high-quality data and the reliance of traditional causal discovery algorithms (TCDA) on the assumption of no latent confounders, as well as their tendency to overlook the precise semantics of latent variables, have long been major obstacles to the broader application of causal discovery. To address this issue, we propose a novel causal modeling framework, TLVD, which integrates the metadata-based reasoning capabilities of large language models (LLMs) with the data-driven modeling capabilities of TCDA for inferring latent variables and their semantics. Specifically, we first employ a data-driven approach to construct a causal graph that incorporates latent variables. Then, we employ multi-LLM collaboration for latent variable inference, modeling this process as a game with incomplete information and seeking its Bayesian Nash Equilibrium (BNE) to infer the possible specific latent variables. Finally, to validate the inferred latent variables across multiple real-world web-based data sources, we leverage LLMs for evidence exploration to ensure traceability. We comprehensively evaluate TLVD on three de-identified real patient datasets provided by a hospital and two benchmark datasets. Extensive experimental results confirm the effectiveness and reliability of TLVD, with average improvements of 32.67% in Acc, 62.21% in CAcc, and 26.72% in ECit across the five datasets.
Paper Structure (41 sections, 2 theorems, 19 equations, 10 figures, 7 tables)

This paper contains 41 sections, 2 theorems, 19 equations, 10 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Causal Discovery
  4. LLM-based Multi-Agent Systems
  5. Traceable Latent Variable Discovery Framework
  6. Identifying Latent Causal Graph Structures
  7. Identifying Latent Variables
  8. Process Definition
  9. BNE Implementation with MALLM
  10. Theoretical Analysis
  11. Verification of Latent Variables
  12. Complexity Analysis
  13. Experiments
  14. Experimental Setup
  15. Datasets & evaluations
  16. ...and 26 more sections

Key Result

Theorem 3.1

(Existence of BNE) In our MALLM framework, assuming certain conditions yi2025from hold, then by Glicksberg’s Fixed Point Theorem ahmad2023common, there exists a BNE strategy profile $\pi^\ast = (\pi^*_1,\dots,\pi^*_N)$. A complete proof is provided in Appendix app-them1.

Figures (10)

  • Figure 1: A toy example of latent variable discovery using tabular data.
  • Figure 2: The overview of TLVD framework.
  • Figure 3: The train process of MALLM.
  • Figure 4: The reasoning process of MALLM.
  • Figure 5: Case study.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Theorem 3.1
  • Lemma 3.1