Research on Computational Social Science

 

  Overview

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, and a simulation method for redistricting.

  Manuscripts and Publications

Machine learning and causality:
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.
Egami, Naoki, and Kosuke Imai. ``Causal Interaction in Factorial Experiments: Application to Conjoint Analysis.''
Ning, Yang, Sida Peng, and Kosuke Imai. ``High Dimensional Propensity Score Estimation via Covariate Balancing.''
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.
Imai, Kosuke, In Song Kim, and Steven Liao. ``Measuring Trade Profiles with Two Billion Observations of Product Trade.''
Markov chain Monte Carlo methods:
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.
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.
Fifield, Benjamin, Michael Higgins, Kosuke Imai, and Alexander Tarr. ``A New Automated Redistricting Simulator Using Markov Chain Monte Carlo.''
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.

     Statistical Software

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
 Last modified: Tue May 9 22:16:23 EDT 2017