10:00 AM - 11:30 AM

As more and more fields like health economics and outcomes research (HEOR) embrace the enormous potential of data science and become increasingly reliant on modern scientific computing tools, there is a deep need to still understand the foundation on which the capabilities of these modern computing tools rest, what “big data” can and cannot deliver and why, and how to realize a potential of machine learning methods for causal inference. The discussion will focus on areas including:

  • The main characteristics of data science and its pertinence to the field of HEOR
  • The capabilities of modern computing tools with regard to big data and statistical methods
  • What “big data” can and cannot deliver and why, informed by the data science insights
  • How to realize a potential of machine learning methods for causal inference

Welcome

Julia Chamova, MBA - Senior Director, Content Strategies, ISPOR — The Professional Society for Health Economics and Outcomes Research

Host

Uwe Siebert, MD, MPH, MSc, ScD - Professor, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria, and Harvard Chan School of Public Health, Boston, MA, USA

Speaker

Judea Pearl, PhD - Professor of computer science and director of the Cognitive Systems Laboratory, Samueli School of Engineering, UCLA, Los Angeles, CA, USA