Abstract: [Purpose/Significance] An in-depth understanding of the causal relationship between the diversity of scientific collaboration and paper novelty is of great significance in guiding the formation of research teams and promoting breakthroughs of original research outcomes of basic research in China. [Design/Methodology] The paper examines the impact of five types of collaborative team diversity: gender diversity, academic age diversity, institutional diversity, disciplinary diversity and topic diversity on paper novelty from the perspectives of population characteristics and tasks. Combined with correlation analysis, regression, and a causal inference method based on the Coarsened Exact Matching（CEM）, it focuses on scientific and technological literature in the field of Artificial Intelligence for empirical analysis. [Findings/Conclusion] In the field of Artificial Intelligence, there is a causal effect between the diversity of collaborative teams in terms of academic age, disciplines, and topics and the novelty of paper. The academic age diversity, disciplines diversity , and topic diversity will respectively result in a novelty gain of 7.66%, 5.98%, and 3.3% for papers in the field of AI. Overall, topic diversity has a greater impact than academic age diversity, which has a greater impact than disciplinary diversity. [Originality/Value] This article reveals the causal relationship between the diversity of scientific collaboration and paper novelty, providing a reference for scientific research team building or scientific research project evaluation, in the field of artificial intelligence or other cross-cutting fields.

Keywords: Paper novelty, Diversity of scientific collaboration, Casual effect, Coarsened Exact Matching, Artificial Intelligence