Regression discontinuity design and its applications to Science of Science: A survey - Li Meiling; Zhang Yang; Wang Yang (Journal of Data and Information Science, 2023)
With the availability of large-scale scholarly datasets, scientists from various domains hope to understand the underlying mechanisms behind science, forming a vibrant area of inquiry in the emerging “science of science” field. As the results from the science of science often has strong policy implications, understanding the causal relationships between variables becomes prominent. However, the most credible quasi-experimental method among all causal inference methods, and a highly valuable tool in the empirical toolkit, Regression Discontinuity Design (RDD) has not been fully exploited in the field of science of science. In this paper, we provide a systematic survey of the RDD method, and its practical applications in the science of science.