# Do Large Language Models Reason Causally Like Us? Even Better?

arXiv: https://arxiv.org/abs/2502.10215 · HTML: https://mlsanity.com/paper/2502.10215/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Causal reasoning in LLMs, one of 17 directions in LLM reasoning.
- Builds on 9 of its 29 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Placed in

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

## Builds on (most cited first, 9 of 9)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models](https://mlsanity.com/paper/2410.05229/) (2024)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [Causal Parrots: Large Language Models May Talk Causality But Are Not Causal](https://mlsanity.com/paper/2308.13067/) (2023)
- [CLadder: Assessing Causal Reasoning in Language Models](https://mlsanity.com/paper/2312.04350/) (2023)
- [A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://mlsanity.com/paper/2406.11050/) (2024)
- [Apple Intelligence Foundation Language Models](https://mlsanity.com/paper/2407.21075/) (2024)
- [Borges and AI](https://mlsanity.com/paper/2310.01425/) (2023)

## Built on by (most cited first, 4 of 4)

- [HCR-Reasoner: Synergizing Large Language Models and Theory for Human-like Causal Reasoning](https://mlsanity.com/paper/2505.08750/) (2025)
- [Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem](https://mlsanity.com/paper/2507.13920/) (2025)
- [Position: Behavioral Systems Require Behavioral Tests](https://mlsanity.com/paper/2608.18081/) (2026)
- [If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models](https://mlsanity.com/paper/2510.08388/) (2025)

## Abstract (arXiv)

Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether their responses reflect true understanding or statistical patterns. We compared causal reasoning in humans and four LLMs using tasks based on collider graphs, rating the likelihood of a query variable occurring given evidence from other variables. LLMs' causal inferences ranged from often nonsensical (GPT-3.5) to human-like to often more normatively aligned than those of humans (GPT-4o, Gemini-Pro, and Claude). Computational model fitting showed that one reason for GPT-4o, Gemini-Pro, and Claude's superior performance is they didn't exhibit the "associative bias" that plagues human causal reasoning. Nevertheless, even these LLMs did not fully capture subtler reasoning patterns associated with collider graphs, such as "explaining away".