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Causal Feature Learning: An Overview

Chalupka, Krzysztof and Eberhardt, Frederick and Perona, Pietro (2017) Causal Feature Learning: An Overview. Behaviormetrika, 44 (1). pp. 137-164. ISSN 0385-7417. https://resolver.caltech.edu/CaltechAUTHORS:20170530-090152248

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Abstract

Causal feature learning (CFL) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. AUAI Press, Edinburgh, pp 181–190, 2015) is a causal inference framework rooted in the language of causal graphical models (Pearl J, Reasoning and inference. Cambridge University Press, Cambridge, 2009; Spirtes et al., Causation, Prediction, and Search. Massachusetts Institute of Technology, Massachusetts, 2000), and computational mechanics (Shalizi, PhD thesis, University of Wisconsin at Madison, 2001). CFL is aimed at discovering high-level causal relations from low-level data, and at reducing the experimental effort to understand confounding among the high-level variables. We first review the scientific motivation for CFL, then present a detailed introduction to the framework, laying out the definitions and algorithmic steps. A simple example illustrates the techniques involved in the learning steps and provides visual intuition. Finally, we discuss the limitations of the current framework and list a number of open problems.


Item Type:Article
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URLURL TypeDescription
https://dx.doi.org/10.1007/s41237-016-0008-2DOIArticle
https://link.springer.com/article/10.1007/s41237-016-0008-2PublisherArticle
http://rdcu.be/s6FxPublisherFree ReadCube access
ORCID:
AuthorORCID
Chalupka, Krzysztof0000-0002-1225-2112
Perona, Pietro0000-0002-7583-5809
Additional Information:© The Behaviormetric Society 2016. Received: 29 August 2016 / Accepted: 29 November 2016 / Published online: 26 December 2016. Communicated by Shohei Shimizu. We thank an anonymous reviewer for pointing out an error in our original theorem. This work was supported by NSF Award #1564330. On behalf of all authors, the corresponding author states that there is no conflict of interest.
Funders:
Funding AgencyGrant Number
NSFIIS-1564330
Subject Keywords:Causal discovery; Causal inference; Graphical models; Bayesian networks; Macrovariables; Multiscale modeling
Issue or Number:1
Record Number:CaltechAUTHORS:20170530-090152248
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170530-090152248
Official Citation:Chalupka, K., Eberhardt, F. & Perona, P. Behaviormetrika (2017) 44: 137. doi:10.1007/s41237-016-0008-2
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:77825
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:30 May 2017 17:16
Last Modified:03 Oct 2019 18:02

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