Direct and indirect effects--an information theoretic perspective
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entropy-22-00854-v2.pdf
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2.42 MB
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Adobe PDF
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Author(s)
Schamberg, Gabriel
Chapman, William
Xie, Shang-Ping
Coleman, Todd P.
Date Issued
July 2020
Journal
Entropy
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Schamberg, Gabriel et al. "Direct and indirect effects--an information theoretic perspective." Entropy 22, 8 (July 2020): 854 ©2020 Author(s)
Version
Final published version
Abstract
Information 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.
MIT Department
Picower Institute for Learning and Memory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/e22080854
