Technical note: Incorporating expert domain knowledge into causal structure discovery workflows
Abstract 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, 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.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Technical note: Incorporating expert domain knowledge into causal structure discovery workflows ; volume:19 ; number:8 ; year:2022 ; pages:2095-2099 ; extent:5
Biogeosciences ; 19, Heft 8 (2022), 2095-2099 (gesamt 5)
- Creator
-
Mäkelä, Jarmo
Melkas, Laila
Mammarella, Ivan
Nieminen, Tuomo
Chandramouli, Suyog
Savvides, Rafael
Puolamäki, Kai
- DOI
-
10.5194/bg-19-2095-2022
- URN
-
urn:nbn:de:101:1-2022042105161853260563
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
- 15.08.2025, 7:27 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Mäkelä, Jarmo
- Melkas, Laila
- Mammarella, Ivan
- Nieminen, Tuomo
- Chandramouli, Suyog
- Savvides, Rafael
- Puolamäki, Kai
Other Objects (12)
Information-Theoretic Causal Discovery
Yavaa : supporting data workflows from discovery to visualization
An introduction to causal discovery
Power analysis for causal discovery
A quantum causal discovery algorithm
Causal Discovery Beyond Conditional Independences
Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery
Causal discovery to understand hot corrosion
Incorporating single molecule in situ hybridization approach to single cell sequencing workflows
Mining Data Wrangling Workflows for Design Patterns Discovery and Specification
Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery
Aristotle: stratified causal discovery for omics data
Information-Theoretic Causal Discovery
Yavaa : supporting data workflows from discovery to visualization
An introduction to causal discovery
Power analysis for causal discovery
A quantum causal discovery algorithm
Causal Discovery Beyond Conditional Independences
Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery
Causal discovery to understand hot corrosion
Incorporating single molecule in situ hybridization approach to single cell sequencing workflows
Mining Data Wrangling Workflows for Design Patterns Discovery and Specification
Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery
Aristotle: stratified causal discovery for omics data
Information-Theoretic Causal Discovery
Yavaa : supporting data workflows from discovery to visualization
An introduction to causal discovery
Power analysis for causal discovery
A quantum causal discovery algorithm
Causal Discovery Beyond Conditional Independences
Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery
Causal discovery to understand hot corrosion
Incorporating single molecule in situ hybridization approach to single cell sequencing workflows
Mining Data Wrangling Workflows for Design Patterns Discovery and Specification
Integrating Computational and Experimental Workflows for Accelerated Organic Materials Discovery