| Autor: |
Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, Kai Puolamäki |
| Přispěvatelé: |
Department of Computer Science, Department of Physics, Institute for Atmospheric and Earth System Research (INAR), Micrometeorology and biogeochemical cycles, Global Atmosphere-Earth surface feedbacks, Helsinki Institute for Information Technology |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Biogeosciences. 19:2095-2099 |
| ISSN: |
1726-4189 |
| DOI: |
10.5194/bg-19-2095-2022 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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