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What is Causal Inference and Where is Data Science Going?

May 27, 2022 @ 12:00 pm - 1:00 pm



Speaker: Judea Pearl
UCLA Computer Science Department
University of California Los Angeles

Date and Time:May 27, 2022 @12:00 PM (PST)

Presentation slides: idre-may2022.pdf

Video recording: https://youtu.be/MNyI1Xkapxg

Abstract: The availability of massive amounts of data coupled with an impressive performance of machine learning algorithms has turned data science into one of the most active research areas in academia. UCLA is no exception. The past few years, however, have uncovered basic limitations in the model-free direction that data science has taken. An increasing number of researchers have come to realize that statistical methodologies and the “black-box” data-fitting strategies used in machine learning are too opaque and brittle and must be enriched by a Causal Inference component to achieve their stated goal: Extract knowledge from data. Interest in Causal Inference has picked up momentum, and it is now one of the hottest topics in data science*.

The purpose of this talk is to tell my colleagues at UCLA, especially IDRE-minded researchers and students, what Causal Inference is all about, how it can be harnessed to solve practical data-scientific problems that cannot be solved by traditional methods, and why it holds the key to the future of data science.

After summarizing some glaring deficiencies of “data fitting” methods, I will contrast them with “model-based” approaches and demonstrate how the latter can achieve a state of knowledge we can call “Deep Understanding”, that is, the capacity to answer questions of three types: predictions, interventions, and counterfactuals.

I will further describe a computational model that facilitates reasoning at these three levels and demonstrate how features normally associated with “understanding” follow from this model. These include generating explanations, generalizing across domains, integrating data from several sources, assigning credit and blame, recovering from missing data, and more. I will conclude by describing future research directions, including automated scientific explorations and personalized decision-making.


Bio sketch: Judea Pearl is Chancellor professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA, where he conducts research in artificial intelligence, human reasoning, and the philosophy of science. He is the author of Heuristics (1983) Probabilistic Reasoning (1988) and Causality (2000,2009) and a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science, The Allen Newell Award from the Association for Computing Machinery, the Benjamin Franklin Medal, the Rumelhart Prize from the Cognitive Science Society, the ACM Turing Award, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect which brings Causal Inference to a general audience.


*Background material:




Institute for Digital Research and Education (IDRE)

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