Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Shaobo Cui, Luca Mouchel, Boi Faltings
Institution: École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412945632