Introduction to causality in science studies

Thomas Klebel and Vincent Traag

No 4bw9e, SocArXiv from Center for Open Science

Abstract: Sound causal inference is crucial for advancing the study of science. Incorrectly interpreting predictive effects as causal might be ineffective or even detrimental to policy recommendations. Many publications in science studies lack appropriate methods to substantiate their causal claims. We here provide an introduction to structural causal models. Such models, usually represented in a graphical form, allow researchers to make their causal assumptions transparent and provide a foundation for causal inference. We illustrate how to use structural causal models to conduct causal inference using regression models based on simulated data of a hypothetical structural causal model of Open Science. The graphical representation of structural causal models allows researchers to clearly communicate their assumptions and findings, thereby fostering further discussion. We hope our introduction helps more researchers in science studies to consider causality explicitly.

Date: 2024-02-09
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:4bw9e

DOI: 10.31219/osf.io/4bw9e

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