Analyzing paper citation using causal inference

Abstract

Because national science and technology policy decisions can be made based on the impact of each technology, quantifying the impact on research is an important task. Citation counts and impact factors can be used to measure the impact of individual studies. What would have happened without the research, however, is fundamentally a counterfactual phenomenon. Thus, we propose a causal inference approach to quantify the research impact of a specific technical topic. We leverage difference-in-difference to quantify the research impact by applying to bibliometric data. Evaluation results show that deep learning significantly affects computer vision and natural language processing. Besides, deep learning significantly affects cross-field citation especially for speech recognition to computer vision and natural language processing to computer vision.