Kosuke Imai's Short Bio


  Short Bio

Kosuke Imai is Professor in the Department of Politics and the Center for Statistics and Machine Learning at Princeton University. At Princeton, he is the founding director of the Program in Statistics and Machine Learning and an executive committee member of the Program for Quantitative and Analytical Political Science (Q-APS). Outside of Princeton, Imai is currently serving as the Vice President and the President-elect of the Society for Political Methodology. He is also Professor of Visiting Status in the Faculty of Law and Graduate Schools of Law and Politics at The University of Tokyo.

After obtaining a B.A. in Liberal Arts from the University of Tokyo (1998), Imai received an A.M. in Statistics (2002) and a Ph.D. in political science (2003) from Harvard University. Imai's research area is political methodology and more generally applied statistics in the social sciences. He has extensively worked on the development and applications of statistical methods for causal inference with experimental and observational data. Other areas of his methodological research are survey methodology and computational algorithms for data-intensive social science research. His substantive applications range from the randomized evaluation of Mexican universal health insurance program to the study of public opinion and insurgent violence in Afghanistan.

Imai is the author of Quantitative Social Science: An Introduction (Princeton University Press, 2017). He has published more than fifty peer-refereed journal articles in political science, statistics, and other fields, and authored over ten open-source software packages. He has won several awards including the Miyake Award (2006), the Warren Miller Prize (2008), the Pi Sigma Alpha Award (2013), the Stanley Kelley, Jr. Teaching Award (2013), the Statistical Software Award (2015), and is the inaugural recipient of Society of Political Methodology's Emerging Scholar Award (2011). Imai's research has been supported by several National Science Foundation grants as well as grants from other agencies.

  Selected Publications

Imai, Kosuke, and David A. van Dyk. (2004). ``Causal Inference With General Treatment Regimes: Generalizing the Propensity Score.'' Journal of the American Statistical Association, Vol. 99, No. 467 (September), pp. 854-866.
Imai, Kosuke, and David A. van Dyk. (2005). ``A Bayesian Analysis of the Multinomial Probit Model Using Marginal Data Augmentation.'' Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334.
Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. (2007). ``Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference.'' Political Analysis, Vol. 15, No.3 (Summer), pp. 199-236. (lead article) Winner of Miller Prize.
Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. (2011). ``Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.'' American Political Science Review, Vol. 105, No. 4 (November), pp. 765-789.
Imai, Kosuke. (2011). ``Multivariate Regression Analysis for the Item Count Technique.'' Journal of the American Statistical Association, Vol. 106, No. 494 (June), pp. 407-416. (featured article)
Imai, Kosuke and Marc Ratkovic. (2014). ``Covariate Balancing Propensity Score.'' Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 76, No. 1 (January), pp. 243-263.

© Kosuke Imai
 Last modified: Sun Feb 12 14:37:09 EST 2017