The 2017 ACM SIGKDD Workshop on Causal Discovery


August 14, 2017, Halifax, Nova Scotia, Canada

Keynote speech

Title: Advances in Causal-Based Feature Selection

Presenter: Ioannis Tsamardinos

Abstract: Feature selection (a.k.a. variable selection) is a common task in data analytics, where the goal is to identify a minimal-size, optimally predictive feature subset. Theoretical results connect the solutions of the problem with the causal mechanism that generated the data, often represented by a Bayesian Network, a Maximal Ancestral Graph, or a Semi-Markov Causal Network. In such frameworks, the selected features are not only predictive of a target outcome of interest but also have a causal interpretation. Such results have given rise to a class of algorithms inspired by causal modeling of the data distribution. In the talk, we examine prototypical causally-inspired feature selection algorithms, advances that allow the algorithms to scale to high-dimensional problems, be applicable to a plethora of different types of data, identify multiple statistically-equivalent solutions, and scale to Big Data.

Biography: Ioannis Tsamardinos, Ph.D., is Associate Professor at the Computer Science Department of University of Crete and co-founder of Gnosis Data Analysis PC, a University start-up. Prof. Tsamardinos’ main research directions include machine learning, bioinformatics, and artificial intelligence. More specifically his work emphasizes variable selection and causal discovery. Prof. Tsamardinos has over 90 publications in international journals, conferences, and books. Prof. Tsamardinos is a regular reviewer for all major Machine Learning and AI conferences (e.g., UAI, ICML, NIPS, KDD, AAAI, IJCAI), leading Machine Learning journals (e.g., JMLR, TKDE, PAMI), and bioinformatics journals (e.g., Bioinformatics Journal, BMC Bioinformatics). Statistics on recognition of work include more than 5000 citations, and h-index of 29 (as estimated by Google Scholar). Ioannis has recently been awarded the European and Greek national grants of excellence, the ERC Consolidator, and the ARISTEIA II grants respectively.