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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...
The Seven Tools of Causal Inference with Reflections on Machine Learning
JUDEA PEARL, UCLA Computer Science Department, USA
ACM Reference Format:
Judea Pearl. 2018. The Seven Tools of Causal Inference with Reflections on
Machine Learning. 1, 1 (November 2018), 6 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn
The dramatic...
Causal inference in statistics: An overview
Abstract: This review presents empirical researchers with recent advances in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special...