Writing

Lecture of Professor Judea Pearl “What is Causal Inference”

Radical Empiricism and Machine Learning Research

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...

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...

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...

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...

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...

Causal Inference Without Counterfactuals: Comment

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...

Generalizing Experimental Results by Leveraging Knowledge of Mechanisms

Abstract We show how experimental results can be generalized across diverse populations by leveraging knowledge of mechanisms that produce the outcome of interest. We use Structural Causal Models (SCM) and a refined version of selection diagrams to...

Graphical Models for Processing Missing Data

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...