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Multi-Level Cause-Effect Systems

Chalupka, Krzysztof and Perona, Pietro and Eberhardt, Frederick (2016) Multi-Level Cause-Effect Systems. Proceedings of Machine Learning Research, 51 . pp. 361-369. ISSN 1938-7228.

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We present a domain-general account of causation that applies to settings in which macro-level causal relations between two systems are of interest, but the relevant causal features are poorly understood and have to be aggregated from vast arrays of micro-measurements. Our approach generalizes that of Chalupka et. al. (2015) to the setting in which the macro-level effect is not specified. We formalize the connection between micro- and macro-variables in such situations and provide a coherent framework describing causal relations at multiple levels of analysis. We present an algorithm that discovers macro-variable causes and effects from micro-level measurements obtained from an experiment. We further show how to design experiments to discover macro-variables from observational micro-variable data. Finally, we show that under specific conditions, one can identify multiple levels of causal structure. Throughout the article, we use a simulated neuroscience multi-unit recording experiment to illustrate the ideas and the algorithms.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Chalupka, Krzysztof0000-0002-1225-2112
Perona, Pietro0000-0002-7583-5809
Additional Information:© 2016 by the authors. KC’s and PP’s work was supported by the ONR MURI grant N00014-10-1-0933.
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-10-1-0933
Record Number:CaltechAUTHORS:20190329-151702979
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94302
Deposited By: Tony Diaz
Deposited On:29 Mar 2019 22:31
Last Modified:03 Oct 2019 21:02

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