NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise

• URL: http://arxiv.org/abs/2605.04313v1
• Date: Tue, 05 May 2026 21:26:01 GMT
• Title: NoisyCausal: A Benchmark for Evaluating Causal Reasoning Under Structured Noise
• Abstract summary: Causal reasoning in natural language requires identifying relevant variables, understanding their interactions, and reasoning about effects and interventions.<n>We introduce NoisyCausal, a new benchmark designed to evaluate causal reasoning under structured noise.<n>We propose a modular reasoning framework that combines LLMs with explicit causal structure to address these challenges.
• Score: 33.130357221784166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Causal reasoning in natural language requires identifying relevant variables, understanding their interactions, and reasoning about effects and interventions, often under noisy or ambiguous conditions. While large language models (LLMs) exhibit strong general reasoning abilities, they struggle to disentangle correlation from causation, particularly when observations are partially incorrect or irrelevant information is present. In this work, we introduce NoisyCausal, a new benchmark designed to evaluate causal reasoning under structured noise. Each instance is generated from a ground-truth causal graph and contextualized with a natural language scenario by injecting controllable forms of noise, such as irrelevant distractors, value perturbations, confounding, and partial observability. Moreover, we propose a modular reasoning framework that combines LLMs with explicit causal structure to address these challenges. Our method prompts the LLM to extract variables, construct a causal graph from context, and then reformulates the reasoning task as a structured prompt grounded in this graph. Rather than relying on statistical patterns alone, the LLM is guided by symbolic structure, enabling more interpretable and robust inference. Experimental results show that our method significantly outperforms standard prompting and reasoning baselines on NoisyCausal. Furthermore, it generalizes well to external benchmarks such as Cladder without task-specific tuning. Our findings highlight the importance of combining causal abstractions with language-driven reasoning to achieve faithful and robust causal understanding in LLMs.

Related papers

• Causal-Audit: Explicit and Auditable Graph-based Reasoning via Target-Aware Causal Chain Construction [14.96995835279229]
  Causal and intervention-based question answering is fundamental to advancing large language models.<n>We propose an explicit and auditable causal reasoning framework for context-free intervention-based question answering.
  arXiv  Detail & Related papers  (2026-04-22T23:24:18Z)
• LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction [4.655574344274635]
  We propose an enhanced causal inference framework that integrates Large Language Model priors with statistical causal discovery.<n>Our proposed method significantly outperforms state-of-the-art baselines in both predictive accuracy and robustness.
  arXiv  Detail & Related papers  (2026-03-12T02:14:16Z)
• 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)
• Causal Prompting for Implicit Sentiment Analysis with Large Language Models [21.39152516811571]
  Implicit Sentiment Analysis (ISA) aims to infer sentiment that is implied rather than explicitly stated.<n>Recent prompting-based methods using Large Language Models (LLMs) have shown promise in ISA.<n>We propose CAPITAL, a causal prompting framework that incorporates front-door adjustment into CoT reasoning.
  arXiv  Detail & Related papers  (2025-07-01T03:01:09Z)
• ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning [47.540945048737434]
  We introduce textttACCESS, a benchmark designed for discovery and reasoning over abstract causal events.<n>We propose a pipeline for identifying abstractions for event generalizations from a large-scale dataset of implicit commonsense causal knowledge.
  arXiv  Detail & Related papers  (2025-02-12T06:19:02Z)
• Failure Modes of LLMs for Causal Reasoning on Narratives [51.19592551510628]
  We investigate the interaction between world knowledge and logical reasoning.<n>We find that state-of-the-art large language models (LLMs) often rely on superficial generalizations.<n>We show that simple reformulations of the task can elicit more robust reasoning behavior.
  arXiv  Detail & Related papers  (2024-10-31T12:48:58Z)
• Zero-shot Causal Graph Extrapolation from Text via LLMs [50.596179963913045]
  We evaluate the ability of large language models (LLMs) to infer causal relations from natural language. LLMs show competitive performance in a benchmark of pairwise relations without needing (explicit) training samples. We extend our approach to extrapolating causal graphs through iterated pairwise queries.
  arXiv  Detail & Related papers  (2023-12-22T13:14:38Z)
• Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension [80.99865844249106]
  We propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism.
  arXiv  Detail & Related papers  (2023-06-21T07:34:27Z)
• Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning [76.00395335702572]
  A central goal for AI and causality is the joint discovery of abstract representations and causal structure. Existing environments for studying causal induction are poorly suited for this objective because they have complicated task-specific causal graphs. In this work, our goal is to facilitate research in learning representations of high-level variables as well as causal structures among them.
  arXiv  Detail & Related papers  (2021-07-02T05:44:56Z)

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.