Detecting low-complexity confounders from data Ruiz Cuevas, M.V. Sokolovska, N. Wuillemin, P.H. /Zucker, Jean-Daniel INTELLIGENCE ARTIFICIELLE INFORMATIQUE SCIENTIFIQUE ANALYSE MULTIVARIABLE MEDECINE BIOLOGIE Statistical dependencies between two variables X and Y indicate that either X causes Y , or Y causes X, or there exists a latent variable Z which influences X and Y. In biology and medicine, an important problem is to find genetic or environmental unobserved causes of phenotypic difference between individuals. In this contribution, we introduce a novel approach to identify unobserved confounders in data. The proposed method is based on the state-of-the-art 3off2 causal network reconstruction algorithm, and on an evidence for a direct causal relation represented by purity of con-ditionals. The proposed method is implemented in Python, and it will be publicly available shortly. We discuss the results obtained on a real biomedical dataset. 2018 text https://www.documentation.ird.fr/hor/fdi:010073292 fdi:010073292 Ruiz Cuevas M.V., Sokolovska N., Wuillemin P.H., Zucker Jean-Daniel. Detecting low-complexity confounders from data. ; 80). 2018, 3 ICML : International Conference on Machine Learning ; CausalML : Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action, 35., Stockholm (SWE), 2018/07/10-15 EN