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canonical_url: "https://aidr.today/8501b797?lang=en"
title: "Stress-Testing the Robustness of LLM-Based Causal Inference"
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published_at: "2026-11-04T05:00:00.000Z"
category: "Research"
topics: ["llm"]
source_urls: ["https://arxiv.org/abs/2611.03656"]
summary: "A new stress-test framework evaluated large language models across six dimensions—model scale, causal graph complexity, prompting strategy, metadata quality, data quality, and sensitivity to uncertainty—and found that models consistently overestimate causal links, resulting in low precision due to an optimism bias."
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# Stress\-Testing the Robustness of LLM\-Based Causal Inference

> [Open the canonical story](<https://aidr.today/8501b797?lang=en>)

**Published:** 2026-11-04T05:00:00.000Z
**Category:** Research
**Topics:** llm

## Summary

A new stress\-test framework evaluated large language models across six dimensions—model scale, causal graph complexity, prompting strategy, metadata quality, data quality, and sensitivity to uncertainty—and found that models consistently overestimate causal links, resulting in low precision due to an optimism bias\.

## Sources

- [Story source](<https://arxiv.org/abs/2611.03656>)