Biogeosciences
Biogeosciences
BG
Articles | Volume 19, issue 8
Articles | Volume 19, issue 8
https://doi.org/10.5194/bg-19-2095-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-2095-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 8
Technical note
 | 
19 Apr 2022
Technical note |  | 19 Apr 2022

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

Jarmo Mäkelä, Laila Melkas, Ivan Mammarella, Tuomo Nieminen, Suyog Chandramouli, Rafael Savvides, and Kai Puolamäki

Viewed

Total article views: 9,100 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
8,520 505 75 9,100 62 57
  • HTML: 8,520
  • PDF: 505
  • XML: 75
  • Total: 9,100
  • BibTeX: 62
  • EndNote: 57
Views and downloads (calculated since 06 Sep 2021)
Cumulative views and downloads (calculated since 06 Sep 2021)

Viewed (geographical distribution)

Total article views: 9,100 (including HTML, PDF, and XML) Thereof 8,749 with geography defined and 351 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
Causal structure discovery algorithms have been making headway into Earth system sciences, and they can be used to increase our understanding on biosphere–atmosphere interactions. In this paper we present a procedure on how to utilize prior knowledge of the domain experts together with these algorithms in order to find more robust causal structure models. We also demonstrate how to avoid pitfalls such as over-fitting and concept drift during this process.
Read more
Altmetrics
Final-revised paper
Preprint