Members

My expertise lies in the application of causal inference—the discipline of considering cause and effect—to medical and epidemiological research. As a physician, I often found myself questioning whether the treatments of interest were truly necessary for the patients in front of me. Driven by the pursuit of an answer to this question, I delved into the field of epidemiology. Between 2016 and 2023, I published 53 papers as the lead or corresponding author and co-authored an additional 26 papers in peer-reviewed international journals.

In this Hakubi Project, I will apply cutting-edge machine learning within the framework of causal inference, aiming to develop a novel treatment strategy focusing on populations with high benefits (or treatment effects) rather than risks. Identifying subpopulations with high benefits is crucial for the allocation of limited medical resources. To this end, we propose a concept called the ‘High Benefit Approach’, which takes into account a comprehensive array of attributes including social circumstances. Through the establishment of the high-benefit approach, I aim to open new frontiers in personalized medicine and significantly transform global healthcare practices, optimizing treatment strategies for the individuals who need them most.