{"ID":2889087,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.21570","arxiv_id":"2507.21570","title":"Causal Link Discovery with Unequal Edge Error Tolerance","abstract":"This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where different types of edge errors may have different importance, current state-of-the-art causal discovery algorithms do not differentiate between the types of edge errors, nor provide any finite-sample guarantees on the edge errors. Hence, this framework seeks to minimize one type of error while keeping the other below a user-specified tolerance level. Using techniques from information theory, fundamental performance limits are found, characterized by the Rényi divergence, for Neyman-Pearson causal discovery. Furthermore, a causal discovery algorithm is introduced for the case of linear additive Gaussian noise models, called epsilon-CUT, that provides finite-sample guarantees on the false positive rate, while staying competitive with state-of-the-art methods.","short_abstract":"This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where different types of edge errors may have different importance, current state-of-the-art causal discovery algorithms do not differentiate between t...","url_abs":"https://arxiv.org/abs/2507.21570","url_pdf":"https://arxiv.org/pdf/2507.21570v1","authors":"[\"Joni Shaska\",\"Urbashi Mitra\"]","published":"2025-07-29T07:59:22Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}