Kosuke Imai

Imai specializes in the development of statistical methods and machine learning algorithms and their applications to social science research. His areas of expertise include causal inference, computational social science, program evaluation, and survey methodology. His substantive applications range from the randomized evaluation of Mexican and Indian national health insurance programs to the assessment of pretrial public safety assessment in the United States criminal justice system. Imai also served as an expert witness in several high-profile legislative redistricting cases in the United States, applying his simulation algorithms. Imai has authored two widely used undergraduate introductory statistics textbooks for social scientists, Quantitative Social Science: An Introduction (Princeton University Press, 2017) and Data Analysis for Social Science: A Friendly and Practical Introduction (with Elena Llaudet; Princeton University Press, 2022).