用因果推断量化科研影响力，揭示深度学习的实际贡献。
A Causal Inference Approach for Quantifying Research Impact
- 基于双重差分法分析论文引用网络，估算技术影响
- 深度学习对计算机视觉和自然语言处理有显著影响
- 发现其影响力是模型可解释性的3.1倍，适合政策研究者
深度学习在十年间推动了计算机科学多个领域的发展，其影响力关乎国家科技政策制定。传统指标如引用量与影响因子难以衡量‘若无此研究会怎样’这一反事实问题。为此，本文提出一种基于因果推断的方法，利用微软学术图谱（Microsoft Academic Graph）中的文献数据，通过关键词或类别标签识别特定技术主题的论文，构建跨领域引用网络，聚合各领域的交叉引用数量，并应用双重差分法评估特定技术主题对各研究领域的影响。实验表明，深度学习显著影响计算机视觉与自然语言处理；尤其在语音识别到计算机视觉、自然语言处理到计算机视觉的跨领域引用中表现突出。此外，本方法揭示深度学习的影响力是机器学习可解释性研究的3.1倍。
原文摘要 · Abstract (English)
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.
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