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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
CausalVerify: An Execution-Grounded Benchmark for LLM Causal Inference Workflows
TL;DR
CausalVerify introduces a benchmark for evaluating causal inference workflows in large language models (LLMs) by assessing whether executed code accurately estimates causal effects. It reveals that while some LLMs perform well, many workflows still yield incorrect estimates.
✦ Why It Matters
Evaluate your LLM's causal inference capabilities using the CausalVerify benchmark to improve accuracy in your workflows.
Key Takeaways
- Use CausalVerify to benchmark LLM performance in causal inference tasks.
- Focus on execution-grounded correctness rather than just code execution.
- Be aware that self-reported confidence may not indicate workflow accuracy.
- Explore the provided datasets and outputs to enhance your own research.
How It Works
CausalVerify evaluates LLMs by pairing real economics research with synthetic scenarios, allowing for a structured assessment of causal estimation workflows. It separates the verification of method descriptions from the actual execution of code, ensuring that the focus is on whether the executed code produces accurate causal estimates.