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Computational Causal Discovery aims to induce causal models,causal networks, and causal relations from observational data withoutperforming or by performing only few interventions (perbutations,manipulations) of a system. While predictive analytics create models thatpredict customer behavior for example, causal analytics create models thatdictate how to affect customer behavior. A recent approach to causaldiscovery, which we call logic-based integrative causal discovery, will bepresented. This approach is more robust to statistical errors, makes morerealistic and less restrictive assumptions (e.g., admits latent confoundingfactors and selection bias in the data) and accepts and reasons withmultiple heterogeneous datasets that are obtained under different samplingcriteria, different experimental conditions (perbubations, interventions),and measuring different quantities (variables). The approach significantlyextends causal discovery based on Bayesian Networks, the simplest causalmodel available, and is much more suitable for real business or scientificdata analysis.
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Prof. Ioannis Tsamardinos is Associate Professor at the ComputerScience Department of University of Crete and co-founder of Gnosis DataAnalysis IKE. Prof. Tsamardinos has over 70 publications in internationaljournals, conferences, and books. He has participated in several national,EU, and US funded research projects. Distinctions with colleagues andstudents include the best performance in one of the four tasks in therecent First Causality Challenge Competition, ISMB 2005 Best Poster Winner,a Gold Medal in the Student Paper Competition in MEDINFO 2004, theOutstanding Student Paper Award in AIPS 2000, the NASA Group AchievementAward for participation in the Remote Agent team and others. He is aregular reviewer for some of the leading Machine Learning journals andconferences. Statistics on recognition of work include more than 4500citations, and h-index of 28 (as estimated by the Publish or Perish tool).Prof. Tsamardinos has recently been awarded the European and Greek nationalgrants of excellence, the ERC Consolidator and the ARISTEIA II grantsrespectively (the equivalent of the NSF Young Investigator Award). Prof.Tsamardinos has pioneered the integrative causal analysis and thelogic-based approach to causal discovery. The ERC grant in particularregards the development of novel integrative causal discovery methods andtheir application to biological mass cytometry data. |
