Technical note: Incorporating expert domain knowledge into causal structure discovery workflows

In this note, we argue that the outputs of causal discovery algorithms should not usually be considered end results but rather starting points and hypotheses for further study. The incentive to explore this topic came from a recent study by Krich et al. (2020), which gives a good introduction to est...

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Bibliographic Details
Published inBiogeosciences Vol. 19; no. 8; pp. 2095 - 2099
Main Authors Mäkelä, Jarmo, Melkas, Laila, Mammarella, Ivan, Nieminen, Tuomo, Chandramouli, Suyog, Savvides, Rafael, Puolamäki, Kai
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 19.04.2022
Copernicus Publications
Subjects
Algorithms
Biosphere
Domains
Knowledge
Noise
Subject specialists
Variables
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Summary:In this note, we argue that the outputs of causal discovery algorithms should not usually be considered end results but rather starting points and hypotheses for further study. The incentive to explore this topic came from a recent study by Krich et al. (2020), which gives a good introduction to estimating causal networks in biosphere–atmosphere interaction but confines the scope by investigating the outcome of a single algorithm. We aim to give a broader perspective to this study and to illustrate how not only different algorithms but also different initial states and prior information of possible causal model structures affect the outcome. We provide a proof-of-concept demonstration of how to incorporate expert domain knowledge with causal structure discovery and remark on how to detect and take into account over-fitting and concept drift.
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ISSN:1726-4189
1726-4170
1726-4189
DOI:10.5194/bg-19-2095-2022