"Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation"

Structured prompting helps LLMs differentiate between correlation and causation through step-by-step analysis.

PC-SUBQ, the proposed prompting strategy, breaks down causal inference into algorithmic steps, helping LLMs determine valid cause-effect relationships from correlation statements with improved accuracy.

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https://arxiv.org/abs/2412.13952

🤔 Original Problem:

LLMs struggle to infer causal relationships from correlation statements, performing poorly on tasks like determining if "Ice cream sales cause shark attacks" from "Ice cream sales correlate with shark attacks."

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🔧 Solution in this Paper:

→ PC-SUBQ decomposes causal inference into 8 fixed subquestions aligned with PC algorithm steps

→ Each subquestion corresponds to a specific step in discovering causal structure

→ The system sequentially prompts LLMs with one subquestion at a time

→ Answers from previous subquestions augment later prompts

→ Few-shot examples guide the LLM through each algorithmic step

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💡 Key Insights:

→ Breaking down complex reasoning into algorithmic steps improves LLM performance

→ Formal causal reasoning can be enhanced through structured prompting

→ The approach is robust to query perturbations and variable renaming

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📊 Results:

→ Outperformed baseline prompting strategies across 5 different LLMs

→ Maintained performance when variable names were modified

→ Showed correct reasoning on natural language examples not seen in training