Beyond correlation: Towards matching strategy for causal inference in Information Science

Dong, Xianlei; Xu, Jiahui; Bu, Yi; Zhang, Chenwei; Ding, Ying; Hu, Beibei; Ding, Yang

doi:10.1177/0165551520979868

Cited by 4 publications

(1 citation statement)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple introductions to structural causal modelling of varying complexity already exist (Rohrer, 2018;Arif and MacNeil, 2023;Elwert, 2013). Dong et al (2022) introduce matching strategies to information science. We believe it is beneficial to introduce causal thinking using familiar examples from science studies, making it easier for researchers in this area to learn about causal approaches.…”

Section: Introductionmentioning

confidence: 99%

Introduction to causality in science studies

Klebel,

Traag

2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%