Machine learning methods for inferring causality patterns from dependency features. Application to causal knowledge discovery in bioinformatics.

Abstract : The relationship between statistical dependency and causality lies at the heart of all statistical approaches to causal inference and can be resumed by two famous statements: correlation (or more generally statistical association) does not imply causation and causation induces a statistical dependency between causes and effects (or more generally descendants). In other terms it is well known that statistical dependency is a necessary yet not sufficient condition for causality. The thesis will explore the use of supervised machine learning techniques to learn causal patterns from features of the probabilistic dependency and will apply them to bioinformatics discovery tasks (e.g. network inference). The potential of this research direction has been demonstrated by the recent Kaggle cause-effect pair competition which showed that causal directionality can be inferred with good accuracy also in Markov indistinguishable configurations. Required expertise: machine learning, statistics, R programming, data mining. Note that at this moment no funding is available for this topic. Please contact me only if you have already a funding source.
Promoteur/Supervisor : Prof. Bontempi Gianluca
Email :
Site Web/Web site :
Centre de recherche/Research center : Machine Learning Group and (IB)^2
Faculté/Faculty : Faculty of Sciences/Faculté des Sciences
Ecole doctorale/Graduate Colleges : Science/Sciences
Ecole doctorale thématique/Graduate School (French Only): Computational Intelligence and Learning (CIL)

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