[2603.24436] Enes Causal Discovery

Computer Science > Neural and Evolutionary Computing arXiv:2603.24436 (cs) [Submitted on 25 Mar 2026] Title:Enes Causal Discovery Authors:Alexis Kafantaris View a PDF of the paper titled Enes Causal Discovery, by Alexis Kafantaris View PDF HTML (experimental) Abstract:Enes The proposed architecture is a mixture of experts, which allows for the model entities, such as the causal relationships, to be further parameterized. More specifically, an attempt is made to exploit a neural net as implementing neurons poses a great challenge for this dataset. To explain, a simple and fast Pearson coefficient linear model usually achieves good scores. An aggressive baseline that requires a really good model to overcome that is. Moreover, there are major limitations when it comes to causal discovery of observational data. Unlike the sachs one did not use interventions but only prior knowledge; the most prohibiting limitation is that of the data which is addressed. Thereafter, the method and the model are described and after that the results are presented. Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Symbolic Computation (cs.SC) Cite as: arXiv:2603.24436 [cs.NE]   (or arXiv:2603.24436v1 [cs.NE] for this version)   https://doi.org/10.48550/arXiv.2603.24436 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Alexios N. Kafantaris [view email] [v1] Wed, 25 Mar 2026 15:47:39 UTC (9...