Multi-Level Cause-Effect Systems
Abstract
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.
Additional Information
© 2016 by the authors. KC's and PP's work was supported by the ONR MURI grant N00014-10-1-0933.Attached Files
Published - chalupka16.pdf
Accepted Version - 1512.07942.pdf
Supplemental Material - chalupka16-supp.pdf
Files
Additional details
- Eprint ID
- 94302
- Resolver ID
- CaltechAUTHORS:20190329-151702979
- Office of Naval Research (ONR)
- N00014-10-1-0933
- Created
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2019-03-29Created from EPrint's datestamp field
- Updated
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2023-06-02Created from EPrint's last_modified field