Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning

2025-06-11

AI Summary

Chain-of-thought (CoT) reasoning often suffers from unreliable intermediate steps—lacking logical sufficiency (failing to fully support the conclusion) and necessity (containing redundant steps). To address this, we propose the first causal-inference-based CoT evaluation framework, introducing *Probability of Sufficiency* (PoS) and *Probability of Necessity* (PoN) to quantify each step’s causal contribution to the final conclusion. Leveraging causal intervention analysis, our method enables automatic completion of missing steps and precise pruning of redundant ones. This work pioneers the systematic integration of causal probabilistic modeling into large language model (LLM) reasoning optimization. Evaluated on mathematical and commonsense reasoning benchmarks, it reduces token consumption by 23.6% on average while preserving accuracy and improving inference efficiency. Our core contribution is a novel, interpretable, and intervention-enabled causal CoT evaluation paradigm, advancing trustworthy and efficient LLM reasoning.

Technology Category

• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that characterizes CoT reasoning through the dual lenses of sufficiency and necessity. Incorporating causal Probability of Sufficiency and Necessity allows us not only to determine which steps are logically sufficient or necessary to the prediction outcome, but also to quantify their actual influence on the final reasoning outcome under different intervention scenarios, thereby enabling the automated addition of missing steps and the pruning of redundant ones. Extensive experimental results on various mathematical and commonsense reasoning benchmarks confirm substantial improvements in reasoning efficiency and reduced token usage without sacrificing accuracy. Our work provides a promising direction for improving LLM reasoning performance and cost-effectiveness.

Problem

• Ensuring reasoning steps sufficiently support conclusions
• Identifying necessary steps for sound reasoning outcomes
• Automating step addition and pruning for efficiency

Innovation

• Causal framework for CoT sufficiency and necessity
• Automated step addition and pruning via causal metrics
• Quantifies step influence under intervention scenarios