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Utilising structural causal models to improve the study of science

Year: 2024

Type: article

Abstract: Causal inference is essential in science studies, yet many publications lack methods to substantiate causal claims. Structural causal models, often represented graphically with directed acyclic graphs, make causal assumptions transparent and improve communication. We illustrate the application with a hypothetical model of Open Science.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13797213

Authors: Klebel, Thomas, Traag, Vincent

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Open Science Impact Pathways

OpenAlex ID: W6949567960

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