ABSTRACT:

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.

This perspective, I hope, will open a robust freeway of communication with other researchers engaged in machine learning and data science.

Background material:

https://ucla.in/2ZLRnyw

https://ucla.in/3iEDRVo

https://ucla.in/2HI2yyx

BIO:

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 and co-authored 5 books on probabilistic and causal reasoning and a fair number of research papers in these areas, accessible on http://bayes.cs.ucla.edu/jp_home.html

Pearl is a member of the National Academy of Sciences, the National Academy of Engineering, a fellow of the IEEE, the ACM, the Royal Statistical Society, the Cognitive Science Society and the Association for the Advancement of Artificial Intelligence.

In 2012, he won the Technion’s Harvey Prize and the ACM Alan Turing Award.

Hosted by Professor Quanquan Gu