Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Alexander Pretschner, Heinz Koeppl, Fakhri Karray. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Previous research on causal reasoning often overlooks the subtleties crucial to understanding causal reasoning. To address this gap, our study introduces the concept of causal epistemic consistency, which focuses on the self-consistency of Large Language Models (LLMs) in differentiating intermediates with nuanced differences in causal reasoning. We propose a suite of novel metrics -- intensity ranking concordance, cross-group position agreement, and intra-group clustering -- to evaluate LLMs on this front. Through extensive empirical studies on 21 high-profile LLMs, including GPT-4, Claude3, and LLaMA3-70B, we have favoring evidence that current models struggle to maintain epistemic consistency in identifying the polarity and intensity of intermediates in causal reasoning. Additionally, we explore the potential of using internal token probabilities as an auxiliary tool to maintain causal epistemic consistency. In summary, our study bridges a critical gap in AI research by investigating the self-consistency over fine-grained intermediates involved in causal reasoning.

Conditional dichotomy, the contrast between two outputs conditioned on the same context, is vital for applications such as debate, defeasible natural language inference, and causal reasoning.Existing methods that rely on semantic similarity often fail to capture the nuanced oppositional dynamics essential for these applications.Motivated by these limitations, we introduce a novel task, Conditional Dichotomy Quantification (ConDQ), which formalizes the direct measurement of conditional dichotomy and provides carefully constructed datasets covering debate, defeasible natural language inference, and causal reasoning scenarios.To address this task, we develop the Dichotomy-oriented Geometric Embedding (DoGE) framework, which leverages complex-valued embeddings and a dichotomous objective to model and quantify these oppositional relationships effectively.Extensive experiments validate the effectiveness and versatility of DoGE, demonstrating its potential in understanding and quantifying conditional dichotomy across diverse NLP applications.Our code and datasets are available at https://github.com/cui-shaobo/ conditional-dichotomy-quantification.

Understanding uncertainty in causality is vital in various domains, including core NLP tasks like event causality extraction, commonsense reasoning, and counterfactual text generation.However, existing literature lacks a comprehensive examination of this area.This survey aims to fill this gap by thoroughly reviewing the uncertainty in causality.We first introduce a novel trichotomy, categorizing causal uncertainty into aleatoric (inherent randomness in causal data), epistemic (causal model limitations), and ontological (existence of causal links) uncertainty.We then survey methods for quantifying uncertainty in causal analysis and highlight the complementary relationship between causal uncertainty and causal strength.Furthermore, we examine the challenges that large language models (LLMs) face in handling causal uncertainty, such as hallucinations and inconsistencies, and propose key traits for an optimal causal LLM.Our paper reviews current approaches and outlines future research directions, aiming to serve as a practical guide for researchers and practitioners in this emerging field.

Luca Mouchel, Debjit Paul, Shaobo Cui, Robert West, Antoine Bosselut, Boi Faltings. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Understanding commonsense causality is a unique mark of intelligence for humans.It helps people understand the principles of the real world better and benefits the decisionmaking process related to causation.For instance, commonsense causality is crucial in judging whether a defendant's action causes the plaintiff's loss in determining legal liability.Despite its significance, a systematic exploration of this topic is notably lacking.Our comprehensive survey bridges this gap by focusing on taxonomies, benchmarks, acquisition methods, qualitative reasoning, and quantitative measurements in commonsense causality, synthesizing insights from over 200 representative articles.Our work aims to provide a systematic overview, update scholars on recent advancements, provide a pragmatic guide for beginners, and highlight promising future research directions in this vital field.A summary of the related literature is available at https://github. com/cui-shaobo/causality-papers . Form ConnectivesCause-Effect Connectives Cause-Effect as, because, cause, since, bring about, due to, lead to, owing to, resulting in Consequence accordingly, as a result, consequently, for this reason, hence, so, therefore, thus Reason in light of, given that, on account of, by reason of, for the sake of, inasmuch as, seeing that Intention so that, in order to, so as to, with the aim of, for the purpose of, with this in mind, in hopes of Conditions if...then, provided that, assuming that, as long as, unless, in the event that Source arises from, stems from, comes from, originates from Counterfactual Connectives Hypothetical had...then, if it hadn't been for, had it not been for, if only Negation were it not for, but for, if it weren't for, without, in the absence of, lacking