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Visual Causal Feature Learning

Chalupka, Krzysztof and Perona, Pietro and Eberhardt, Frederick (2015) Visual Causal Feature Learning. In: UAI'15 Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. AUAI Press , Arlington, VA, pp. 181-190. ISBN 978-0-9966431-0-8. https://resolver.caltech.edu/CaltechAUTHORS:20190327-085913684

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Abstract

We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed from micro-variables. We prove the Causal Coarsening Theorem, which allows us to gain causal knowledge from observational data with minimal experimental effort. The theorem provides a connection to standard inference techniques in machine learning that identify features of an image that correlate with, but may not cause, the target behavior. Finally, we propose an active learning scheme to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior. We illustrate our inference and learning algorithms in experiments based on both synthetic and real data.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://dl.acm.org/citation.cfm?id=3020867PublisherAbstract
http://arxiv.org/abs/1412.2309arXivDiscussion Paper
ORCID:
AuthorORCID
Chalupka, Krzysztof0000-0002-1225-2112
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2015 AUAI Press. KC’s work was funded by the Qualcomm Innovation Fellowship 2014. KC’s and PP’s work was supported by the ONR MURI grant N00014-10-1-0933. FE would like to thank Cosma Shalizi for pointers to many relevant results this paper builds on.
Funders:
Funding AgencyGrant Number
Qualcomm Inc.UNSPECIFIED
Office of Naval Research (ONR)N00014-10-1-0933
Record Number:CaltechAUTHORS:20190327-085913684
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190327-085913684
Official Citation:Krzysztof Chalupka, Pietro Perona, and Frederick Eberhardt. 2015. Visual causal feature learning. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15), Marina Meila and Tom Heskes (Eds.). AUAI Press, Arlington, Virginia, United States, 181-190.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94199
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:27 Mar 2019 17:15
Last Modified:03 Oct 2019 21:01

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