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Session 11: Causality, Artificial Intelligence, and Big Data
Session Chair(s)
Rima Izem, PhD
Associate Director Statistical Methodology
Novartis, Switzerland
Representative Invited
FDA, United States
William Wang, PhD
Executive Director
Merck & Co, Inc, United States
Life is full of intended or unintended cause and effect. Understanding and utilizing these causal relationships have been at the core of human learning and human intelligence. With great technology advance in this digital age, big data is fueling our imagination and innovation for Machine Learning and Artificial Intelligence. In this session, we will invite a few top experts to examine what all these mean for our pursuit of causality. We will ask the questions of why, what, how we should handle causal inference with/without randomization in the context of biopharmaceutical research. Various approaches in design and analysis for causal inference will be discussed. We will also look to the future and discuss the role biopharmaceutical statisticians should play in the machine powered data driven casual inference for pharmaceutical innovation.
Speaker(s)
Causal Inference and Data-Fusion
Elias Bareinboim
Purdue University, United States
Professor
Targeted Machine Learning for Generating Real-World Evidence from Observational Data
Mark Johannes van der Laan, PhD
UC Berkeley, United States
Professor in Biostatistics and Statistics