Writing

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Radical Empiricism and Machine Learning Research

13 Mar, 2021

Judea Pearl – University of California, Los Angeles Computer Science Department Abstract I contrast the “data fitting” vs. “data interpreting” approaches to data-science along three dimensions: Expediency, Transparency and Explainability.“Data...

Causal Thinking in the Twilight Zone

29 May, 2020

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...

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

28 May, 2020

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...

Causal Inference Without Counterfactuals: Comment

06 May, 2020

Judea Pearl 1. BACKGROUND The field of statistics has seen many well-meaning crusades against threats from metaphysics and other heresy. In its founding prospectus of 1834, the Royal Statistical Society resolved “to exclude carefully all Opinions...

Graphical Models for Processing Missing Data

18 Apr, 2020

This paper reviews recent advances in missing data research using graphical models to represent multivariate dependencies. We first examine the limitations of traditional frameworks from three different perspectives: \textit{transparency, estimability...