A tutorial on learning causal influence

Richard E. Neapolitan; Xia Jiang

doi:10.1007/10985687_2

A tutorial on learning causal influence

Richard E. Neapolitan^*, Xia Jiang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

In the 1990's related research in artificial intelligence, cognitive science, and philosophy resulted in a method for learning causal relationships from passive data when we have data on at least four variables. We illustrate the method using a few simple examples. Then we present recent research showing that we can even learn something about causal relationships when we have data on only two variables.

Original language	English (US)
Title of host publication	Innovations in Machine Learning
Subtitle of host publication	Theory and Applications
Editors	Dawn Holmes, Lakhmi Jain
Pages	29-71
Number of pages	43
DOIs	https://doi.org/10.1007/10985687_2
State	Published - 2006

Publication series

Name	Studies in Fuzziness and Soft Computing
Volume	194
ISSN (Print)	1434-9922

ASJC Scopus subject areas

Computer Science (miscellaneous)
Computational Mathematics

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10.1007/10985687_2

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title = "A tutorial on learning causal influence",

abstract = "In the 1990's related research in artificial intelligence, cognitive science, and philosophy resulted in a method for learning causal relationships from passive data when we have data on at least four variables. We illustrate the method using a few simple examples. Then we present recent research showing that we can even learn something about causal relationships when we have data on only two variables.",

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A tutorial on learning causal influence

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