On the 14th of August 2017 Prof. Tsamardinos will give an invited talk at the 2017 ACM SIGKDD Workshop on Causal Discovery. Title: “Advances in Causal-Based Feature Selection”
The talk will illustrate the past and explore the future of feature selection algorithms based on Causal Discovery theory.
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.