Horizon 1 1 10 C-ACTI : Communications avec actes dans un congrès international ICML : International Conference on Machine Learning ; CausalML : Machine Learning for Causal Inference, Counterfactual Prediction, and Autonomous Action 3 INTELLIGENCE ARTIFICIELLE INFORMATIQUE SCIENTIFIQUE ANALYSE MULTIVARIABLE MEDECINE BIOLOGIE 2018 2018/07/10-15 fdi:010073292 ENG 80 https://www.documentation.ird.fr/hor/fdi:010073292 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers18-08/010073292.pdf Horizon (IRD) 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. 122INTAR