Sokolovska N. auteur aut IRD Permiakova O. auteur aut IRD Forslund S. K. auteur aut IRD Zucker Jean-Daniel auteur aut IRD text journalArticle eng text/pdf reformatted digital access An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem of causal inference in a bivariate case, where the joint distribution of two variables is observed. We consider, in particular, data on discrete domains. The state-of-the-art causal inference methods for continuous data suffer from high computational complexity. Some modern approaches are not suitable for categorical data, and others need to estimate and fix multiple hyper-parameters. In this contribution, we introduce a novel method of causal inference which is based on the widely used assumption that if X causes Y, then P(X) and P(Y vertical bar X) are independent. We propose to explore a semi-supervised approach where P(Y vertical bar X) and P(X) are estimated from labeled and unlabeled data respectively, whereas the marginal probability is estimated potentially from much more (cheap unlabeled) data than the conditional distribution. We validate the proposed method on the standard cause-effect pairs. We illustrate by experiments on several benchmarks of biological network reconstruction that the proposed approach is very competitive in terms of computational time and accuracy compared to the state-of-the-art methods. Finally, we apply the proposed method to an original medical task where we study whether drugs confound human metagenome. specialized Causal inference semi-supervised learning probabilistic models metagenomic data 020 122 17 1 358-364 2020 1545-5963 https://www.documentation.ird.fr/hor/PAR00020756 10.1109/tcbb.2018.2879504 1545-5963 [F B010080283] https://www.documentation.ird.fr/hor/PAR00020756 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers21-04/010080283.pdf IRD - Base Horizon / Pleins textes 2020-06-09 2025-02-24 PAR00020756 fre