Hal Varian (Google) speaks at the Feb. 2017 Data on Purpose / Do Good Data conference “From Possibilities to Responsibilities” presented by Stanford Social Innovation Review and the Digital Civil Society Lab at the Stanford Center on Philanthropy and Civil Society. For more information on the conference, visit ssirdata.org.
In this session, Varian discusses the conceptual framework required to establish causal inference and computational methods that can allow causality to be inferred. Hal's presentation explores the possibility of testing causality in large data settings, and raises certain basic questions: Will access to massive data be a key to understanding the fundamental questions of basic and applied science? Or does the vast increase in data confound analysis, produce computational bottlenecks, and decrease the ability to draw valid causal inferences?