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.

相关性已成为信息科学的基本方法。然而，在做出具体决策时，相关性是有限的。在本文中，我们详细介绍了如何在信息科学领域使用因果推理。因果推断匹配的实现主要有六个步骤，即选择候选控制变量、确定控制变量、计算所有样本之间的相似度、形成控制组、检查控制组的性能和估计因果效应。例如，本文应用因果推理来调查诺贝尔物理学奖是否会增加奖后引用。该方法以分步方式呈现，以便研究人员可以在未来重现我们的分析。