What is Causal Inference? - A Logical Perspective
Judea Pearl, Samueli School of Engineering, University of California-Los Angeles
The purpose of this talk is to explain the role of causal inference in the context of growing interests in machine learning and data science.
I will treat causal inference as a new branch of logic, thriving upon its own semantics, grammar, and computational tools, and capable of quantifying its own capabilities and limitations. I will then demonstrate how the new logic has changed the thinking in many of the sciences and how practical problems relying on causal information, including challenges in machine learning, can now be solved using elementary mathematics.
About the Speaker
Dr. Judea Pearl is Chancellor's professor of computer science and statistics at UCLA, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human cognition, and philosophy of science. He has authored numerous scientific papers and three books, Heuristics (1983), Probabilistic Reasoning (1988) and Causality (2000, 2009), which won the London School of Economics Lakatos Award in 2002. More recently, he co-authored Causal Inference in Statistics (2016, with M. Glymour and N. Jewell) and The Book of Why (2018, with Dana Mackenzie), which brings causal analysis to the general audience. Pearl is a member of the National Academy of Sciences and the National Academy of Engineering, a fellow of the Cognitive Science Society, and a founding fellow of the Association for the Advancement of Artificial Intelligence. In 2012, he won the Technion's Harvey Prize and the ACM Alan Turing Award for the development of a calculus for probabilistic and causal reasoning.
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