From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911214369505280 |
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| author | Hiremath, Sujai Janzing, Dominik Faller, Philipp Blöbaum, Patrick Kirschbaum, Elke Kasiviswanathan, Shiva Prasad Gan, Kyra |
| author_facet | Hiremath, Sujai Janzing, Dominik Faller, Philipp Blöbaum, Patrick Kirschbaum, Elke Kasiviswanathan, Shiva Prasad Gan, Kyra |
| contents | Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guarantees for existing methods require perfect predictions or uncertainty estimates, making them unreliable for practical use. We propose the Guess2Graph (G2G) framework, which uses expert guesses to guide the sequence of statistical tests rather than replacing them. This maintains statistical consistency while enabling performance improvements. We develop two instantiations of G2G: PC-Guess, which augments the PC algorithm, and gPC-Guess, a learning-augmented variant designed to better leverage high-quality expert input. Theoretically, both preserve correctness regardless of expert error, with gPC-Guess provably outperforming its non-augmented counterpart in finite samples when experts are "better than random." Empirically, both show monotonic improvement with expert accuracy, with gPC-Guess achieving significantly stronger gains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples? Hiremath, Sujai Janzing, Dominik Faller, Philipp Blöbaum, Patrick Kirschbaum, Elke Kasiviswanathan, Shiva Prasad Gan, Kyra Machine Learning Artificial Intelligence Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guarantees for existing methods require perfect predictions or uncertainty estimates, making them unreliable for practical use. We propose the Guess2Graph (G2G) framework, which uses expert guesses to guide the sequence of statistical tests rather than replacing them. This maintains statistical consistency while enabling performance improvements. We develop two instantiations of G2G: PC-Guess, which augments the PC algorithm, and gPC-Guess, a learning-augmented variant designed to better leverage high-quality expert input. Theoretically, both preserve correctness regardless of expert error, with gPC-Guess provably outperforming its non-augmented counterpart in finite samples when experts are "better than random." Empirically, both show monotonic improvement with expert accuracy, with gPC-Guess achieving significantly stronger gains. |
| title | From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples? |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.14488 |