This book comprehensively presents the current state of the arts and real-world cases of the usage of causal inference in science of science from a multidisciplinary perspective and discusses how this methodology can further change the research and practice in science of science in the future. Science of science has evolved from disciplines such as information science, statistics, sociology, and psychology, and is an interdisciplinary discipline that studies the scientific ecosystem itself. Science of science not only emphasizes theoretical research but also places special emphasis on the application of technical methods. Science of science is constantly absorbing and applying the methodology of causal inference from other disciplines. Quantitative research in the field of science of science cannot be limited to examining "what"-type research questions through descriptive statistics, linear regression, and correlation coefficient measurements. It is urgent to strengthen the analysis and interpretation capabilities of causal-level issues, namely "why"-type research questions. The application of causal inference in the field of science of science can help validate and enrich existing scientific theories and enhance the level of policy/decision support in this discipline.

Yi Bu is an Assistant Professor in Data Science at the Department of Information Management, Peking University, China. Before joining Peking University, he was a research fellow at the Center for Science of Science and Innovation, Northwestern Institute on Complex Systems, and the Kellogg School of Management, Northwestern University. He is doing research in the application aspect of big data analytics, with a particular focus on scholarly data mining. Specifically, his research endeavors to elucidate the process of knowledge diffusion, the analysis of scholarly networks and their variants, and bibliometric indicators for research assessment, with an emphasis on issues of diversity, equity, and inclusion. He aims to understand the social dimensions of the global scientific ecosystem by leveraging massive datasets, computational techniques, and social theories. He was supported by the Young Elite Scientists Sponsorship Program by the China Association for Science and Technology (2021). He has been leading projects supported by the National Science Foundation of China, the Ministry of Education of China, and a variety of enterprises. He is serving as the Associate Editor-in-chief of Editing Practice, an editorial board member of the Annual Review of Information Science and Technology, Nature Humanities and Social Sciences Communications, and the Journal of Information Science, an advisory board member of the Journal of the Association for Information Science and Technology, and a member of the Board of Director of the International Society Network for the Science of Team Science (INSciTS). He has an undergraduate degree in Information Management and System from Peking University, an M.S. in Data Science, and a Ph.D. in Informatics from Indiana University Bloomington.

Zhiyu Tao is a Ph.D. student in Information Science at Peking University, under the supervision of Prof. Danqun Zhao and Prof. Yi Bu. His research interests focus on the Science of Science, technology management, and innovation policy. Prior to joining Peking University, he received an M.S. in Information Science from the Chinese Academy of Sciences, a B.Mngt. from Yunnan University, and a B.S. from Chiang Mai University. He is a recipient of the Development Research Scholarship, the National Graduate Student Scholarship, as well as Research Project Awards from the China Federation of Logistics. Meanwhile, Zhiyu Tao is actively engaged in both the academic community and information practice. He provides information consultancy to several agencies and serves as a reviewer for several SCI/SSCI journals.

Jinyu Gao is a Ph.D. student in the College of Information Science at the University of Arizona. She received her M.S. in Management Science from the University of Chinese Academy of Sciences in 2023 and her B.S. in Financial Engineering from Tianjin University of Finance and Economics in 2020.

Danqun Zhao is a Professor in Information Science at the Department of Information Management, Peking University. Her research interests lie in information retrieval, informetrics and scientometrics, data mining, and information analysis. Over the years, she has been devoted to the study of information organization and knowledge discovery, with particular attention to citation analysis, research evaluation, and the application of quantitative methods in information science. She has published extensively in these areas and has also contributed to the development of teaching materials in information retrieval and related fields.