Integrating Causal Reasoning into Automated Fact-Checking

• URL: http://arxiv.org/abs/2512.13286v1
• Date: Mon, 15 Dec 2025 12:56:00 GMT
• Title: Integrating Causal Reasoning into Automated Fact-Checking
• Authors: Youssra Rebboud, Pasquale Lisena, Raphael Troncy,
• Abstract summary: In fact-checking applications, a common reason to reject a claim is to detect the presence of erroneous cause-effect relationships between the events at play.<n>We propose a methodology that combines event relation extraction, semantic similarity computation, and rule-based reasoning to detect logical inconsistencies between chains of events mentioned in a claim and in an evidence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In fact-checking applications, a common reason to reject a claim is to detect the presence of erroneous cause-effect relationships between the events at play. However, current automated fact-checking methods lack dedicated causal-based reasoning, potentially missing a valuable opportunity for semantically rich explainability. To address this gap, we propose a methodology that combines event relation extraction, semantic similarity computation, and rule-based reasoning to detect logical inconsistencies between chains of events mentioned in a claim and in an evidence. Evaluated on two fact-checking datasets, this method establishes the first baseline for integrating fine-grained causal event relationships into fact-checking and enhance explainability of verdict prediction.

Related papers

• CausalFlip: A Benchmark for LLM Causal Judgment Beyond Semantic Matching [50.65932158912512]
  We propose a new causal reasoning benchmark, CausalFlip, to encourage the development of new large language models.<n>CaulFlip consists of causal judgment questions built over event triples that could form different confounder, chain, and collider relations.<n>We evaluate LLMs under multiple training paradigms, including answer-only training, explicit Chain-of-Thought supervision, and a proposed internalized causal reasoning approach.
  arXiv  Detail & Related papers  (2026-02-23T18:06:15Z)
• Event Causality Identification with Synthetic Control [48.18257618831177]
  Event causality identification (ECI) is crucial for distinguishing causation from correlation.<n>Traditional approaches to ECI have primarily utilized linguistic patterns and multi-hop relational inference.<n>In this paper, we adopt the Rubin Causal Model to identify event causality.
  arXiv  Detail & Related papers  (2025-09-16T22:05:52Z)
• Causal Cartographer: From Mapping to Reasoning Over Counterfactual Worlds [9.153187514369849]
  Causal world models can answer counterfactual questions about an environment of interest.<n>It requires understanding the underlying causes behind chains of events and conducting causal inference for unseen distributions.<n>We show that our approach can extract causal knowledge while reducing inference costs and spurious correlations.
  arXiv  Detail & Related papers  (2025-05-20T14:14:05Z)
• Self-Compatibility: Evaluating Causal Discovery without Ground Truth [28.72650348646176]
  We propose a novel method for falsifying the output of a causal discovery algorithm in the absence of ground truth. Our key insight is that while statistical learning seeks stability across subsets of data points, causal learning should seek stability across subsets of variables. We prove that detecting incompatibilities can falsify wrongly inferred causal relations due to violation of assumptions or errors from finite sample effects.
  arXiv  Detail & Related papers  (2023-07-18T18:59:42Z)
• Towards Trustworthy Explanation: On Causal Rationalization [9.48539398357156]
  We propose a new model of rationalization based on two causal desiderata, non-spuriousness and efficiency. The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets.
  arXiv  Detail & Related papers  (2023-06-25T03:34:06Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Fairness and robustness in anti-causal prediction [73.693135253335]
  Robustness to distribution shift and fairness have independently emerged as two important desiderata required of machine learning models. While these two desiderata seem related, the connection between them is often unclear in practice. By taking this perspective, we draw explicit connections between a common fairness criterion - separation - and a common notion of robustness.
  arXiv  Detail & Related papers  (2022-09-20T02:41:17Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Nested Counterfactual Identification from Arbitrary Surrogate Experiments [95.48089725859298]
  We study the identification of nested counterfactuals from an arbitrary combination of observations and experiments. Specifically, we prove the counterfactual unnesting theorem (CUT), which allows one to map arbitrary nested counterfactuals to unnested ones.
  arXiv  Detail & Related papers  (2021-07-07T12:51:04Z)
• Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction [54.06292969184476]
  We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Our approach employs semantic loss which captures the precise meaning of a logical sentence. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.
  arXiv  Detail & Related papers  (2021-03-20T00:16:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.