School of Information and Data Sciences, Nagasaki University

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Research Activities

Staff Introduction

  • Institute of Integrated Science and Technology, Associate Professor
  • School of Information and Data Sciences, Associate Professor
  • Ph.D. in Science and Technology
  • M.A. in Political Science
  • B.A. in Philosophy
Researcher number
Date of arrival
Nagasaki University: Apr.2020-
Areas of Research
Statistical Science, Political Methodology, Causal Inference, Missing Data
BA in Philosophy, Keio University
MA in Political Science, California State University, Los Angeles
Doctoral candidate (Political Methodology), Michigan State University
Senior Researcher, National Statistics Center
Specially-appointed Assistant Professor, IR Office, Tokyo University of Foreign Studies
Ph.D. in Science and Technology, Seikei University
Specially-appointed Associate Professor (Lecturer), Center for Innovative Teaching and Learning, Tokyo Institute of Technology
Professional Survey Statistician
Associate Professor, Department of Economics, The International University of Kagoshima
Japan Society of Economic Statistics Award

Research activities

Theory and Implementation of Causal Inference in Statistics (Published by Kyoritsu Shuppan)
  • What should we do in order to increase the number of tourists to Nagasaki? Just as this question, in order to change Factor Y, we need to manipulate another Factor X. In this situation, we say that Y is the outcome and X is the cause, i.e., causality.
  • The 2021 Nobel prize in economics was awarded to two econometricians, Joshua Angrist and Guido Imbens for their methodological contributions to the analysis of causal relationships. Causal inference has attracted attention in many fields around the world.
  • This book discusses causal inference in terms of both theory (mathematical mechanism) and implementation (numerical analysis in R) in a coherent manner. The book has become very popular right after publication; thus, reprinted just a few days after publication.
Proposal of a novel method for causal inference (New finding in statistics)
  • Suppose that we want to know the effect of a new drug. It is impossible to simultaneously examine the two outcomes, one with taking the drug and the other without taking the drug. Causal inference is an important goal in many scientific fields; however, we need to think in terms of the difference in these two potential outcomes. Nevertheless, only one outcome of the potential outcomes is observed; thus, causal inference is said to be the problem of missing data.
  • Multiple imputation has been known as a method to deal with missing data, but little is known about how we can use multiple imputation to estimate the local average treatment effect at the cutoff. Therefore, I propose a new causal inference technique, named multiple imputation regression discontinuity design. To implement the approaches, I have written easy-to-use software, R-package MIRDD.
  • The finding was published in a statistics journal with an impact factor, Communications in Statistics – Simulation and Computation (Taylor & Francis, This method can be applied to many fields, such as the analysis of incumbency advantage, the analysis of the relationship between education and poverty, the analysis of the effect of Covid-19 vaccination.
Figure 1: Observed Data (Left) and Simulation of Potential Outcomes (Right)
Figure 2: Observed Data and Simulation in the Non-Treated Group (Left), and Observed Data and Simulation in the Treated Group (Right)
Vertical line in black: Cutoff
Vertical dashed line in green: Local area
○ in gray: Observations in the non-treated group
△ in blue: Observations in the treated group
○ in red: Simulated values in the non-treated group
△ in red: Simulated values in the treated group

Educational activities

School of Information and Data Sciences:
First-year Seminar, Technical English I/II, Bayesian Statistics, Social and Tourism Informatics Ⅲ, Research Project
Liberal Arts Education:
Introduction to Data Science, Introduction to Statistics
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