AIWS University

Impede the Motion of Data and You Impede Innovation

The original article can be found here. Impede the Motion of Data and You Impede Innovation by Ravi Naik Why that is, and what to do about it. Data drives innovation. At scale, innovation does not happen in isolation. Without finely tuned, smartly orchestrated...

The Seven Tools of Causal Inference, with Reflections on Machine Learning

A PDF of this paper can be found here. The Seven Tools of Causal Inference, with Reflections on Machine Learning by Judea Pearl The dramatic success in machine learning has led to an explosion of artificial intelligence (AI) applications and increasing...

Video Highlights: Machine Learning and Causal Inference

In the IDSS Distinguished Seminar, Professor Susan Athey reviewed a series of recent papers that develop new methods based on Machine Learning methods to approach problems of causal inference, including estimation of conditional average treatment effects...

Telling and Re-telling History: The case for a whiggish account of the history of causation

Judea Pearl Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles, CA 90024 USA [email protected] (Co-authored with Dana Mackenzie) March 12, 2019 Abstract This note was originally written for The Book...

Professor Judea Pearl’s tweets

Glad you asked. I have been in this business for 3 decades and have not found anything clearer, more rigorous or friendlier than Chapter 4 of Primer. Here is a free link https://t.co/Sf5yw328zl. Enjoy, and don't let the mystics tell you counterfacuals...

Efficient Intervention Design for Causal Discovery with Latents

Raghavendra Addanki, Shiva Prasad Kasiviswanathan, Andrew McGregor, Cameron Musco Professor Judea Pearl wrote:”An interesting paper on causal discovery using both observations and interventions. The problem is to recover a causal graph while...

Plenary 2: The Mathematics of Causal Inference: with Reflections on Machine Learning

Professor Judea Pearl wrote:In my Microsoft lecture here, I talked about statistics as the “calculus of shadows” and causality as a 3D calculus. I can think of several 4D’s and 5D’s, like object-based and agent-based calculi. But...

What Statisticians Want to Know about Causal Inference and The Book of Why

I was privileged to be interviewed recently by David Hand, Professor of Statistics at Imperial College, London, and a former President of the Royal Statistical Society. I would like to share this interview with readers of this blog since many of the questions...

Causal Thinking in the Twilight Zone

Judea Pearl University of California, Los Angeles Computer Science Department Los Angeles, CA, 90095-1596, USA (310) 825-3243 / [email protected] To students of causality, the writings of William Cochran provide an excellent and intriguing vantage...