MuPlon: Multi-Path Causal Optimization for Claim Verification through Controlling Confounding

2025-09-29

AI Summary

Traditional fact-checking methods overlook complex interactions among evidential statements and rely on fully connected graphs, making them vulnerable to data noise and confounding biases—thus compromising verification reliability. To address this, we propose MuPlon, a multi-path causal optimization framework that introduces dual causal interventions—backdoor adjustment and front-door counterfactual reasoning—into claim verification for the first time. Specifically, MuPlon mitigates confounding bias via node-weight optimization along backdoor paths and alleviates noise sensitivity through counterfactual inference along front-door paths. The method integrates causal graph modeling, probabilistic weighted node selection, relevant subgraph extraction, and counterfactual reasoning to establish an interpretable, multi-path inference mechanism. Evaluated on multiple benchmark datasets, MuPlon achieves state-of-the-art performance, significantly outperforming existing approaches. Our results empirically validate that causal intervention is pivotal for enhancing the robustness and reliability of automated fact-checking systems.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

As a critical task in data quality control, claim verification aims to curb the spread of misinformation by assessing the truthfulness of claims based on a wide range of evidence. However, traditional methods often overlook the complex interactions between evidence, leading to unreliable verification results. A straightforward solution represents the claim and evidence as a fully connected graph, which we define as the Claim-Evidence Graph (C-E Graph). Nevertheless, claim verification methods based on fully connected graphs face two primary confounding challenges, Data Noise and Data Biases. To address these challenges, we propose a novel framework, Multi-Path Causal Optimization (MuPlon). MuPlon integrates a dual causal intervention strategy, consisting of the back-door path and front-door path. In the back-door path, MuPlon dilutes noisy node interference by optimizing node probability weights, while simultaneously strengthening the connections between relevant evidence nodes. In the front-door path, MuPlon extracts highly relevant subgraphs and constructs reasoning paths, further applying counterfactual reasoning to eliminate data biases within these paths. The experimental results demonstrate that MuPlon outperforms existing methods and achieves state-of-the-art performance.

Problem

• Addressing data noise and biases in claim verification
• Optimizing evidence interactions through causal intervention strategies
• Improving reliability by controlling confounding factors in graphs

Innovation

• Multi-path causal optimization for claim verification
• Back-door path dilutes noise via node weight optimization
• Front-door path eliminates bias with counterfactual reasoning