Beyond correlation: Towards matching strategy for causal inference in Information Science
Xianlei Dong, Jiahui Xu, Yi Bu, Chenwei Zhang, Ying Ding, Beibei Hu, Yang Ding
Abstract
Correlation has become a fundamental method for information science. However, correlations are limited in making concrete decisions. In this article, we detail how causal inference could be utilised in the field of information science. There are six main steps of implementing matching for causal inference, namely, selecting candidate control variables, determining control variables, calculating similarities among all samples, forming control group, examining the performance of control group and estimating causal effects. As an example, this article applies causal inference to investigate whether Nobel Physics award increases the after-award citations. The method is presented in a step-by-step manner so that researchers can reproduce our analysis in the future.