Data science is a discipline that involves extracting valuable information from data and utilizing it for decision-making. The purposes of data analysis vary widely. Machine learning has been highly successful in making predictions based on correlations and is widely used in society. However, understanding causal relationships, not just correlations, is also crucial for informed decision-making.

Our research group focuses on developing mathematical methodologies to uncover the causal mechanisms underlying natural phenomena and human behavior based on data. In particular, we work on designing mathematical frameworks for inferring causal relationships from observational data without intervention, aiming to surpass the limitations of conventional methods and establish new methodological foundations.

Furthermore, we collaborate with researchers across various scientific disciplines to address fundamental problems in natural and social sciences, as well as applied fields such as engineering and medicine.Our goal is to contribute to problem-solving from a methodological perspective. We are also actively engaged in industry-government-academia collaborations to tackle practical challenges.