Research Advance of Causal Inference in Clinical Medicine: A Bibliometrics Analysis via Citespace

Abstract

Guoqiang Qin,1 Jianxiang Wei,1,2 Yuehong Sun,3 Wenwen Du1 1School of Management, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China; 2Library, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China; 3School of Mathematical Sciences, Nanjing Normal University, Nanjing, People’s Republic of ChinaCorrespondence: Jianxiang Wei, School of Management, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China, Email [email protected] Yuehong Sun, School of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, People’s Republic of China, Email [email protected]: Causal inference in clinical medicine provides scientific evidence for precision medicine and individualized treatment by revealing the true associations between interventions and health outcomes. This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, primary themes, and future directions for the application of causal inference in clinical medicine.Methods: We conducted a literature search in the Web of Science database using causal inference and medical terminology as subject keywords, covering the period from January 1986 to December 2024. After screening, we obtained 4,316 documents for analysis. Utilizing CiteSpace to generate network diagrams, we analyzed data related to the number of publications, citation analysis, collaboration relationships, keyword co-occurrence, and highlighted terms to illustrate the knowledge map and collaboration network in this field.Results: Publications on medical causal inference shows a fluctuating growth trend over time. The United States was the top contributors to this field. Harvard University is the leading research institution. George David Smith is the most prolific author, Robbins JM is the most cited scholar. The major research hotspots concentrated in fields such as epidemiology, coronary heart disease and health. Notably, marginal structural models, counterfactual forecasting, and Mendelian randomization have consistently been key methodologies in research. The burstness of keywords reveals that big data, DNA methylation, and robust estimation are emerging research directions.Conclusion: In clinical research, counterfactual forecasting provides prospective guidance for optimizing clinical strategies; Mendelian randomization helps uncover potential therapeutic targets; and marginal structural models enhance the accuracy of causal effect estimation in clinical studies. The future integration of various data sources to improve causal inference methods is anticipated to enhance the sensitivity and specificity of trials, ultimately elucidating the complex mechanisms of diseases and drug effects. The literature retrieve strategy and the metrics of the tools adopted may have a certain impact on the results of this study.Keywords: causal inference, counterfactual, marginal structural model, Mendelian randomization, bibliometrics

Keywords

• causal inference
• counterfactual
• marginal structural model
• mendelian randomization
• bibliometrics