Causality and Machine Learning

Type Colloquium
Start Date March 1, 2017 03:00 PM
End Date March 1, 2017 04:00 PM
Location 5317 Sennott Square
Organizer Name
Speaker Name Prof. Kun Zhang
Speaker Affiliation Carnegie Mellon University
Abstract Can we find the causal direction between two variables? How can we make optimal predictions in the presence of distribution shift? We are often faced with such causal modeling or prediction problems. Recently, with the rapid accumulation of huge volumes of data, both causal discovery, i.e., learning causal information from purely observational data, and machine learning are seeing exciting opportunities as well as great challenges. This talk will be focused on recent advances in causal discovery and how causal information facilitates understanding and solving certain problems of learning from heterogeneous data.

In particular, I will talk about conditional independence-based and functional causal model-based approaches to causal discovery, focusing on their underlying assumptions, algorithms, and applications. Practical issues in causal discovery, including selection bias and nonstationarity or heterogeneity of the data, will also be addressed. Finally, I will discuss why and how underlying causal knowledge helps in learning from heterogeneous data when the i.i.d. assumption is dropped, with transfer learning? as a particular example.

Bio: Kun Zhang is an assistant professor in the philosophy department and the machine learning department (affiliated) of Carnegie Mellon University (CMU), USA. Before joining CMU, he was a senior research scientist at Max Planck Institute for Intelligent Systems, Germany, and a lead scientist at Information Sciences Institute of University of Southern California. His main research interests include causal analysis, machine learning, artificial intelligence, and computational finance. He has made a series of contributions in solving some long-standing problems in causality, such as how to distinguish cause from effect and how to make nonparametric conditional independence test reliable. He has served as a senior program committee member or area chair for a number of conferences in machine learning or artificial intelligence, and organized various academic activities to foster interdisciplinary research in causality.