Benchmarking and Enhancing Agentic Causal Discovery
LLM agents can now run causal discovery algorithms on a dataset and report what comes out.
ProofPaper ↗
Key points
- Whether they do more than that, such as noticing where an algorithm went wrong and fixing it with further tests, is hard to tell from current benchmarks.
- We introduce Heuresis, a benchmark of anonymous simulated datasets whose mechanisms range from linear Gaussian to heavy-tailed.
- For each family of datasets it separates what a better existing algorithm can gain from what only a closer look at the data can gain, and it traces every edge an agent changes.
- We instantiate the benchmark with an agent harness and three LLMs, GPT-6.1 Sol, Claude Opus 5.5 and Qwen 3.8 Flash.
Sources (1)
- [1]Benchmarking and Enhancing Agentic Causal DiscoveryarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 02:47 AM
LLM agents can now run causal discovery algorithms on a dataset and report what comes out.
Whether they do more than that, such as noticing where an algorithm went wrong and fixing it with further tests, is hard to tell from current benchmarks.
Extractive summary: sentences quoted from the sources.
Before this
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