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dc.contributor.authorSchamberg, Gabriel
dc.contributor.authorChapman, William
dc.contributor.authorXie, Shang-Ping
dc.contributor.authorColeman, Todd P.
dc.date.accessioned2020-09-23T17:32:00Z
dc.date.available2020-09-23T17:32:00Z
dc.date.issued2020-07
dc.identifier.issn1099-4300
dc.identifier.urihttps://hdl.handle.net/1721.1/127685
dc.description.abstractInformation theoretic (IT) approaches to quantifying causal influences have experienced some popularity in the literature, in both theoretical and applied (e.g., neuroscience and climate science) domains. While these causal measures are desirable in that they are model agnostic and can capture non-linear interactions, they are fundamentally different from common statistical notions of causal influence in that they (1) compare distributions over the effect rather than values of the effect and (2) are defined with respect to random variables representing a cause rather than specific values of a cause. We here present IT measures of direct, indirect, and total causal effects. The proposed measures are unlike existing IT techniques in that they enable measuring causal effects that are defined with respect to specific values of a cause while still offering the flexibility and general applicability of IT techniques. We provide an identifiability result and demonstrate application of the proposed measures in estimating the causal effect of the El Niño–Southern Oscillation on temperature anomalies in the North American Pacific Northwest.en_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.relation.isversionof10.3390/e22080854en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceMultidisciplinary Digital Publishing Instituteen_US
dc.titleDirect and indirect effects--an information theoretic perspectiveen_US
dc.typeArticleen_US
dc.identifier.citationSchamberg, Gabriel et al. "Direct and indirect effects--an information theoretic perspective." Entropy 22, 8 (July 2020): 854 ©2020 Author(s)en_US
dc.contributor.departmentPicower Institute for Learning and Memoryen_US
dc.relation.journalEntropyen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-08-21T13:50:57Z
dspace.date.submission2020-08-21T13:50:57Z
mit.journal.volume22en_US
mit.journal.issue8en_US
mit.licensePUBLISHER_CC


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