AIWS University

BAYESIANISM AND CAUSALITY, OR, WHY I AM ONLY A HALF-BAYESIAN

In D. Corfield and J. Williamson (Eds.) Foundations of Bayesianism, Kluwer Applied Logic Series, Kluwer Academic Publishers,Vol. 24, 19-36, 2001. TECHNICAL REPORT R-284 July 2001   JUDEA PEARL   1 INTRODUCTION I turned Bayesian in 1971, as soon...

Professor Judea Pearl and 2-year anniversary of “The Book of Why”

On May 20, 2018, “The Book of Why” by professor Judea Pearl was published. Governor Michael Dukakis said on March 20, 2020: “The Book of Why: The New Science of Cause and Effect,” provides us with the new tools needed to navigate the uncharted...

THE SILENT HISTORY OF CAUSE AND EFFECT

Judea Pearl UCLA HPASS Los Angeles, CA Outline The Ubiquity of causal thinking in ethics and science, from ancient times to modernity. Why it kept silent till 1920. What prevented statisticians, economists, social and health scientists from answering...

Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects

Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf Professor Judea Pearl wrote: “This paper is the first COVID-19 analysis I read that goes beyond data-fitting and tells society: Yes! AI can be trusted to extract meaning from data, and can do...

A Guided Tour of Artificial Intelligence Research

Volume I: Knowledge Representation, Reasoning and Learning Introduction The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues....

Pearl, Judea and Mackenzie, Dana: The book of why: the new science of cause and effect (2018)

Basic Books, New York. ISBN 9780465097609 (hardcover) Karamjit S. Gill  [email protected] University of Brighton, Brighton, UK © Springer-Verlag London Ltd., part of Springer Nature 2020 ‘The Book of Why’ by Judea Pearl and Dana Mackenzie...

What are the differences between econometrics, statistics, and machine learning?

In comparing econometrics, statistics, and machine learning methodologies, one must distinguish between standard and advanced machine learning. The former, exemplified by deep learning and neural networks, fits a function to a stream of data and plays...

On The Reasons Behind Decisions

Adnan Darwiche, Auguste Hirth Professor Judea Pearl commented “Speaking about “explainable AI”, this paper shows that, even in classification tasks, and even after agreeing on a Bayesian Network classifier, answering “why”...

Causal Inference in Machine Learning and AI

Online lecture on causal data science wtih directed acyclic graphs (DAGs) in the advanced applied econometrics course at KU Leuven, Belgium (May 7, 2020). Talk is based on the following paper: https://arxiv.org/abs/1912.09104 Further teaching material:...