Virtual Talking Data Series: Professor Steve Tadelis, UC Berkeley Haas School of Business

Machine Learning, Correlation, and Causation

Machine Learning and Big Data are terms that have entered the lexicon of every business leader and executive across the globe. Companies are scrambling to hire data scientists and data analysts, and are hiring all means of consulting firms to try and build some sort of data-driven culture. Yet the abuses of data science are as prevalent as its successful uses, and demystifying some of the key strengths and weaknesses is critical to better employ these powerful tools. This session will demystify some of the ways in which Machine Learning can be used and misused in business and decision support applications.

About Our Speaker

Professor Steve Tadelis is a Professor of Economics and the Sarin Chair in Leadership and Strategy at UC Berkeley's Haas School of Business. He is also a Distinguished Fellow at Melbourne Business School's Centre for Business Analytics. Steve was previously the Associate Dean for Strategic Planning (2006-2009) at UC Berkeley and also taught at Stanford University for eight years.

Steve also held positions as a Senior Director and Distinguished Economist at eBay Research Labs (2011-2013) and Vice President of Economics and Market Design at Amazon (2016-2017) where he applied economic research tools to a variety of product and business applications, working with technologists, machine learning scientists, and business leaders. He continues to advise Amazon part-time as an Amazon Economist Fellow.

Steve’s areas of research span e-commerce, the economics of organization, procurement contracting, industrial organization, contract theory, and game theory.