Teaching Causality - Part II with Judea Pearl and Rob Gould


  • Author: Statistics Views
  • Date: 13 May 2015
  • Copyright: Statistics Views. Image appears courtesy of Getty Images.

Judea Pearl, Professor of Computer Science and Statistics and director of the Cognitive Systems Laboratory at UCLA, who championed the development of Bayesian networks, and later developed a calculus for causal inference, describes how best to teach causality to students to Rob Gould, Undergraduate Vice-Chair of the Department of Statistics and Director of the Center for Teaching Statistics at UCLA.

In 2013, the American Statistical Association announced an annual Causality in Statistics Education Prize, "to encourage the teaching of basic causal inference methods in introductory statistics courses."

thumbnail image: Teaching Causality - Part II with Judea Pearl and Rob Gould

Pearl, who donated the prize said the prize is aiming to close a growing gap between research and education in this field. "While researchers are swept in an unprecedented excitement over new causal inference tools that are unveiled before us almost daily, the excitement is hardly seen among statistics educators, and is totally absent from statistics textbooks."

"I am determined" he said, "to convince every statistics instructor that causation is easy (It is!) and that he/she too can teach it for fun and profit. The fun comes from showing students how simple mathematical tools can answer questions that Pearson-Fisher-Neyman could not begin to address (e.g., control of confounding, model diagnosis, Simpson's paradox, mediation analysis), and the profit comes because most customers of statistics ask causal, not associational, questions."

Here in the second part of the video, Professor Pearl offers guidance to every statistics instructor on how best to teach causality.

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