Introduction to structural causal models in science studies

Abstract Sound causal inference is crucial for advancing the study of science. Incorrectly interpreting predictive effects as causal might lead to ineffective or even detrimental policy recommendations. Many publications in science studies lack appropriate methods to substantiate causal claims. We here provide an introduction to structural causal models for science studies. Structural causal 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.