# Nuance Matters: Probing Epistemic Consistency in Causal Reasoning

arXiv: https://arxiv.org/abs/2409.00103 · HTML: https://mlsanity.com/paper/2409.00103/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning.
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Placed in

- LLM reasoning / [Causal reasoning in LLMs](https://mlsanity.com/lr/causal-reasoning-in-llms/)

## Builds on (most cited first, 5 of 5)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [Look Within, Why LLMs Hallucinate: A Causal Perspective](https://mlsanity.com/paper/2407.10153/) (2024)
- [A Logical Fallacy-Informed Framework for Argument Generation](https://mlsanity.com/paper/2408.03618/) (2024)
- [The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning](https://mlsanity.com/paper/2406.19307/) (2024)

## Built on by (most cited first, 1 of 1)

- [Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States](https://mlsanity.com/paper/2607.26929/) (2026)

## Abstract (arXiv)

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.