A Causal Inference Approach for Quantifying Research Impact

Abstract

Deep learning has had a great impact on various fields of computer science by enabling data-driven representation learning in a decade. Because science and technology policy decisions for a nation can be made on the impact of each technology, quantifying research impact is an important task. The number of citations and impact factor can be used to measure the impact for individual research. What would have happened without the research, however, is fundamentally a counterfactual phenomenon. Thus, we propose an approach based on causal inference 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. First, we identify papers of a specific technical topic using keywords or category tags from Microsoft Academic Graph, which is one of the largest academic publication dataset. Next, we build a paper citation network between each technical field. Then, we aggregate the cross-field citation count for each research field. Finally, the impact of a specific technical topic for each research field is estimated by applying difference-in-difference. 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. Moreover, our method revealed that the impact of deep learning was 3.1 times of the impact of interpretability for ML models.