Over the last two decades, the amount and variety of data available to social scientists have dramatically increased. While in the 1990s most researchers were analyzing a handful of national surveys and government data, today's quantitative social scientists conduct their own randomized experiments and surveys and analyze a diverse array of large-scale data sets, ranging from textual to spatial data. This emerging trend demands new statistical methodologies that enable social scientists to overcome these data analytical and computational challenges.
I have developed fast and reliable computational methods for popular Bayesian models such as the multinomial probit and ecological inference models. I have also worked on the development of computational methods for lage-scale data sets in social science research. They include the fast and scalable estimation of various ideal point models for massive data, a dynamic clustering method for large scale product-level trade data, a dynamic regression model for networks, analyses of textual and video data, simulation and enumeration methods for redistricting, and a method for record linkage with large-scale administrative data.
Heterogeneous treatment effects:
Imai, Kosuke, and Aaron Strauss. (2011). ``Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-out-the-vote Campaign.'' Political Analysis, Vol. 19, No. 1 (Winter), pp. 1-19. (lead article) Winner of Political Analysis Editors' Choice Award.
Imai, Kosuke and Marc Ratkovic. (2013). ``Estimating Treatment Effect Heterogeneity in Randomized Program Evaluation.'' Annals of Applied Statistics, Vol. 7, No. 1 (March), pp. 443-470. Winner of the Tom Ten Have Memorial Award.
Imai, Kosuke and Michael Lingzhi Li. ``Experimental Evaluation of Individualized Treatment Rules.''
Egami, Naoki, and Kosuke Imai. (2019). ``Causal Interaction in Factorial Experiments: Application to Conjoint Analysis.'' Journal of the American Statistical Association, Vol. 114, No. 526 (June), pp. 529-540.
de la Cuesta, Brandon, Naoki Egami, and Kosuke Imai. ``Experimental Design and Statistical Inference for Conjoint Analysis: The Essential Role of Population Distribution..'' Political Analysis, Forthcoming.
Highdimensional propensity score:
Ning, Yang, Sida Peng, and Kosuke Imai. ``Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score..'' Biometrika, Forthcoming.
Clustering and scaling methods for large-scale data:
Imai, Kosuke, James Lo, and Jonathan Olmsted. (2016). ``Fast Estimation of Ideal Points with Massive Data.'' American Political Science Review, Vol. 110, No. 4 (December), pp. 631-656.
Kim, In Song, Steven Liao, and Kosuke Imai. (2020). ``Measuring Trade Profile with Granular Product-level Trade Data.'' American Journal of Political Science, Vol. 64, No. 1 (January), pp. 102-117.
Olivella, Santiago, Tyler Pratt, and Kosuke Imai. ``Dynamic Stochastic Blockmodel Regression for Social Networks: Application to International Conflicts..''
Analysis of textual and video data:
Hwang, June, Kosuke Imai, and Alex Tarr. ``Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation Study.''
Eshima, Shusei, Kosuke Imai, and Tomoya Sasaki. ``Keyword Assisted Topic Models.''
Fifield, Benjamin, Kosuke Imai, Jun Kawahara, and Christopher T. Kenny. ``The Essential Role of Empirical Validation in Legislative Redistricting Simulation.'' Statistics and Public Policy, Forthcoming.
Fifield, Benjamin, Michael Higgins, Kosuke Imai, and Alexander Tarr. ``Automated Redistricting Simulation Using Markov Chain Monte Carlo.'' Journal of Computational and Graphical Statistics, Forthcoming.
Record linkage methods:
Enamorado, Ted, Benjamin Fifield, and Kosuke Imai. (2019). ``Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records.'' American Political Science Review, Vol. 113, No. 2 (May), pp. 353-371.
Enamorado, Ted, and Kosuke Imai. (2019). ``Validating Self-reported Turnout by Linking Public Opinion Surveys with Administrative Records.'' Public Opinion Quarterly, Vol. 83, No. 4 (Winter), pp. 723–748.
Multinomial probit models:
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.
Imai, Kosuke, and David A. van Dyk. (2005). ``MNP: R Package for Fitting the Multinomial Probit Model.'' Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. abstract reprinted in Journal of Computational and Graphical Statistics, (2005) Vol. 14, No. 3 (September), p. 747.
Ecological inference models:
Imai, Kosuke, and Gary King. (2004). ``Did Illegal Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?.'' Perspectives on Politics, Vol. 2, No. 3 (September), pp.537-549. Our analysis is a part of The New York Times article, ``How Bush Took Florida: Mining the Overseas Absentee Vote'' By David Barstow and Don van Natta Jr. July 15, 2001, Page 1, Column 1.
Imai, Kosuke, Ying Lu, and Aaron Strauss. (2008). ``Bayesian and Likelihood Inference for 2 x 2 Ecological Tables: An Incomplete Data Approach.'' Political Analysis, Vol. 16, No. 1 (Winter), pp. 41-69.
Imai, Kosuke, Ying Lu, and Aaron Strauss. (2011). ``eco: R Package for Ecological Inference in 2 x 2 Tables.'' Journal of Statistical Software, Vol. 42, No. 5 (Special Volume on Political Methodology), pp. 1-23.
Imai, Kosuke and Kabir Khanna. (2016). ``Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Record.'' Political Analysis, Vol. 24, No. 2 (Spring), pp. 263-272.
Imai, Kosuke, Ying Lu, and Aaron Strauss. ``eco: R Package for Ecological Inference in 2 x 2 Tables.'' available through The Comprehensive R Archive Network. 2004-2009.
Imai, Kosuke, and David A. van Dyk. ``MNP: R Package for Fitting the Multinomial Probit Model.'' available through The Comprehensive R Archive Network. 2004-2008.
Khanna, Kabir, and Kosuke Imai. ``wru: Who Are You? Bayesian Predictions of Racial Category Using Surname and Geolocation.'' available through GitHub. 2015.
Fifield, Benjamin, Alexander Tarr, Michael Higgins, and Kosuke Imai. ``redist: Markov Chain Monte Carlo Methods for Redistricting Simulation.'' available through The Comprehensive R Archive Network and the GitHub. 2015.
Imai, Kosuke, James Lo, and Jonathan Olmsted. ``emIRT: EM Algorithms for Estimating Item Response Theory Models.'' available through The Comprehensive R Archive Network and the GitHub. 2015.
© Kosuke Imai