Kosuke Imai's Publications and Manuscripts

Dasanaike, Noah and Kosuke Imai (2026). ``Using Embedding Models to Improve Probabilistic Race Prediction.''

Shi, Wenqi, Kosuke Imai, and Yi Zhang (2026). ``Privacy-preserving Meta-analysis through Low-Rank Basis Hunting.''

O'Sullivan, Philip, Kosuke Imai, and Cory McCartan (2026). ``Generalized Sequential Monte Carlo Sampling for Redistricting Simulation.''

Sengupta, Souhardya, Kosuke Imai, and Georgia Papadogeorgou (2025). ``Low-rank Covariate Balancing Estimators under Interference.''

Miyazaki, Sho, Kento Yamada, and Kosuke Imai (2025). ``Estimating the Partisan Bias of Japanese Legislative Redistricting Plans Using a Simulation Algorithm.''

Mukaigawara, Mitsuru, and Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou (2025). ``Spatiotemporal causal inference with arbitrary spillover and carryover effects.''

Koch, Benedikt and Kosuke Imai (2025). ``Statistical Decision Theory with Counterfactual Loss.''

Zhou, Lingxiao, and Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou (2025). ``Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference: How Economic Assistance Moderates the Effects of Airstrikes on Insurgent Violence.''

Imai, Kosuke and Kentaro Nakamura (2025). ``Gen-AI Powered Inference.''

Imai, Kosuke and Kentaro Nakamura (2025). ``Causal Representation Learning with Generative Artificial Intelligence: Application to Texts as Treatments.''

Jasny, Ethan, Christopher T. Kenny, Cory McCartan, Tyler Simko, Melissa Wu, Michael Y. Zhao, Aneetej Arora, Emma Ebowe, Philip O'Sullivan, Taran Samarth, and Kosuke Imai (2025). ``Gerrymandering and geographic polarization have reduced electoral competition.''

Li, Michael Lingzhi and Kosuke Imai (2025). ``Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning.''

Jia, Zeyang, Kosuke Imai, and Michael Lingzhi Li (2025). ``Cramming Contextual Bandits for On-policy Statistical Evaluation.''

Zhang, Yi and Kosuke Imai (2025). ``Individualized Policy Evaluation and Learning under Clustered Network Interference.''

Jiang, Zhichao, Eli Ben-Michael, D. James Greiner, Ryan Halen, Kosuke Imai, and Zhichao Jiang (2025). ``Longitudinal Causal Inference with Selective Eligibility.''

Zhang, Yi, Melody Huang, and Kosuke Imai (2024). ``Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data.''

Zhang, Yi, Eli Ben-Michael, and Kosuke Imai (2024). ``Safe Policy Learning under Regression Discontinuity Designs.''

Chattopadhyay, Ambarish, Kosuke Imai, and Jose R. Zubizarreta (2024). ``Design-based inference for generalized network experiments with stochastic interventions.''

Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li (2023). ``Distributionally Robust Causal Inference with Observational Data.''

Malani, Anup, Phoebe Holtzman, Kosuke Imai, Cynthia Kinnan, Morgen Miller, Shailender Swaminathan, Alessandra Voena, Bartosz Woda, and Gabriella Conti (2021). ``Effect of Health Insurance in India: A Randomized Controlled Trial.''

Llaudet, Elena, and Kosuke Imai (2022). ``Data Analysis for Social Science: A Friendly and Practical Introduction.'' Princeton University Press. Translated into Japanese (2025).

Imai, Kosuke (2017). ``Quantitative Social Science: An Introduction.'' Princeton University Press. Translated into Japanese (2018), Chinese (2020), and Korean (2021).

Jia, Zeyang, Eli Ben-Michael, and Kosuke Imai ``Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Forthcoming.

Lo, Adeline, Santiago Olivella, and Kosuke Imai ``A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate.'' Political Analysis, Forthcoming.

McCartan, Cory, Christopher Kenny, Tyler Simko, Emma Ebowe, Michael Zhao, and Kosuke Imai ``Redistricting Reforms Reduce Gerrymandering by Constraining Partisan Actors.'' American Political Science Review, Forthcoming.

Blackwell, Matthew, Jacob R. Brown, Sophie Hill, Kosuke Imai, and Teppei Yamamoto (2025). ``Priming bias versus post-treatment bias in experimental designs.'' Political Analysis, Vol. 33, No. 4 (October), pp. 361-377.

Ben-Michael, Eli, D. James Greiner, Kosuke Imai, and Zhichao Jiang (2025). ``Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment.'' Journal of the American Statistical Association, Vol. 120, No. 551, pp. 1386-1399.

Ben-Michael, Eli, D. James Greiner, Melody Huang, Kosuke Imai, Zhichao Jiang, Sooahn Shin (2025). ``Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies.'' Proceedings of the National Academy of Sciences, Vol. 122, No. 38, e2505106122.

Breuer, Adam, Bryce J. Dietrich, Michael H. Crespin, Matthew Butler, J.A. Pyrse, Kosuke Imai (2025). ``Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952-2012.'' Scientific Data, Vol. 12, No. 1552.

Goplerud, Max, Kosuke Imai, Nicole E. Pashley (2025). ``Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis.'' Annals of Applied Statistics, Vol. 19, No. 2 (June), pp. 866-888.

Imai, Kosuke and Michael Lingzhi Li (2025). ``Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments.'' Journal of Business & Economic Statistics, Vol. 43, No. 1, pp. 256-268.

McCartan, Cory, Robin Fisher, Jacob Goldin, Daniel E. Ho, Kosuke Imai (2025). ``Estimating Racial Disparities When Race is Not Observed.'' Journal of the American Statistical Association, Vol. 120, No. 552, pp. 2140-2153.

Tarr, Alexander and Kosuke Imai (2025). ``Estimating Average Treatment Effects with Support Vector Machines.'' Statistics in Medicine, Vol. 44, No. 5, e70006.

McCartan, Cory, Jacob Brown, and Kosuke Imai (2024). ``Measuring and Modeling Neighborhoods.'' American Political Science Review, Vol. 118, No. 4 (November), pp. 1966-1985.

Ham, Dae Woong, Kosuke Imai, and Lucas Janson (2024). ``Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis.'' Political Analysis, Vol. 32, No. 3 (July), pp. 329-344.

Johnson, Rebecca A., Tyler Simko, and Kosuke Imai (2024). ``A Summer Bridge Program for First-Generation Low-Income Students Stretches Academic Ambitions with No Adverse Impacts on GPA.'' Proceedings of the National Academy of Sciences, Vol. 121, No. 50, e2404924121.

Ben-Michael, Eli, Kosuke Imai, and Zhichao Jiang (2024). ``Policy Learning with Asymmetric Counterfactual Utilities.'' Journal of the American Statistical Association, Vol. 119, No. 548, pp. 3045-3058.

Kenny, Christopher, Cory McCartan, Tyler Simko, and Kosuke Imai (2024). ``Census officials must constructively engage with independent evaluations.'' Proceedings of the National Academy of Sciences (Letter), Vol. 121, No. 11, e2321196121.

Li, Michael Lingzhi and Kosuke Imai (2024). ``Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules.'' Journal of Causal Inference, Vol 12, No. 1, pp. 1-20. Special Issue on Neyman (1923) and its influences on causal inference

Kenny, Christopher, Cory McCartan, Shiro Kuriwaki, Tyler Simko, and Kosuke Imai (2024). ``Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods.'' Science Advances, Vol 10, No. 18 (May), pp. 1-13.

Eshima, Shusei, Kosuke Imai, and Tomoya Sasaki (2024). ``Keyword-Assisted Topic Models.'' American Journal of Political Science, Vol. 68, No. 2 (April), pp. 730-750.

McCartan, Cory, Tyler Simko, and Kosuke Imai (2024). ``Rejoinder: We Can Improve the Usability of the Census Noisy Measurements File.'' Harvard Data Science Review, Vol. 6, No. 2 (Spring).

McCartan, Cory and Kosuke Imai (2023). ``Sequential Monte Carlo for Sampling Balanced and Compact Redistricting Plans.'' Annals of Applied Statistics, Vol. 17, No. 4 (December), pp. 3300-3323.

McCartan, Cory, Tyler Simko, and Kosuke Imai (2023). ``Making Differential Privacy Work for Census Data Users.'' Harvard Data Science Review, Vol. 5, No. 4 (Fall).

Tarr, Alexander, June Hwang, and Kosuke Imai (2023). ``Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation Study.'' Political Analysis, Vol. 31, No. 4 (October), pp. 554-574.

Jiang, Zhichao, Kosuke Imai, and Anup Malani (2023). ``Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments.'' Biometrics, Vol. 79, No. 3 (September), pp. 2370-2381.

Imai, Kosuke and Zhichao Jiang (2023). ``Principal Fairness for Human and Algorithmic Decision-Making.'' Statistical Science, Vol. 38, No. 2 (July), pp317-328.

Imai, Kosuke, In Song Kim, and Erik Wang (2023). ``Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.'' American Journal of Political Science, Vol. 67, No. 3 (July), pp. 587-605.

Fan, Jianqing, Kosuke Imai, Inbeom Lee, Han Liu, Yang Ning, and Xiaolin Yang (2023). ``Optimal Covariate Balancing Conditions in Propensity Score Estimation.'' Journal of Business & Economic Statistics, Vol. 41, No. 1, pp. 97-110.

Imai, Kosuke and Michael Lingzhi Li (2023). ``Experimental Evaluation of Individualized Treatment Rules.'' Journal of the American Statistical Association, Vol. 118, No. 541, pp. 242-256.

Kenny, Christopher T., Cory McCartan, Tyler Simko, Shiro Kuriwaki, and Kosuke Imai (2023). ``Widespread Partisan Gerrymandering Mostly Cancels Nationally, but Reduces Electoral Competition.'' Proceedings of the National Academy of Sciences, Vol. 120, No. 25, e2217322120.

McCartan, Cory, Tyler Simko, and Kosuke Imai (2023). ``Researchers need better access to US Census data.'' Science, Vol. 380, No. 6648 pp. 902-903.

Rosenman, Evan T.R., Santiago Olivella, and Kosuke Imai (2023). ``Race and ethnicity data for first, middle, and last names.'' Scientific Data, Vol. 10, No. 299, pp. 1-11.

Imai, Kosuke, Zhichao Jiang, D. James Greiner, Ryan Halen, and Sooahn Shin (2023). ``Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment.'' (with discussion) Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 186, No. 2 (April), pp. 167-189. Read before the Royal Statistical Society.

Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan Rosenman, Tyler Simko, and Kosuke Imai (2023). ``Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System.'' Harvard Data Science Review, Special Issue 2: Dierential Privacy for the 2020 U.S. Census (January).

Imai, Kosuke, Santiago Olivella, and Evan T.R. Rosenman (2022). ``Addressing Census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements.'' Science Advances, Vol. 8, No. 49 (December), pp. 1-10.

Papadogeorgou, Georgia, Kosuke Imai, Jason Lyall, and Fan Li (2022). ``Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq.'' Journal of the Royal Statistical Society, Series B (Statistical Methodology), Vol. 84, No. 5 (November), pp. 1969-1999.

McCartan, Cory, Christopher T. Kenny, Tyler Simko, George Garcia III, Kevin Wang, Melissa Wu, Shiro Kuriwaki, and Kosuke Imai (2022). ``Simulated redistricting plans for the analysis and evaluation of redistricting in the United States.'' Scientific Data, Vol. 9, No. 689, pp. 1-10.

Olivella, Santiago, Tyler Pratt, and Kosuke Imai (2022). ``Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts.'' Journal of the American Statistical Association, Vol. 117, No. 539, pp. 1068-1081.

de la Cuesta, Brandon, Naoki Egami, and Kosuke Imai (2022). ``Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution.'' Political Analysis, Vol. 30, No. 1 (January), pp. 19-45.

Kenny, Christopher T., Shiro Kuriwaki, Cory McCartan, Evan T.R. Rosenman, Tyler Simko, and Kosuke Imai (2021). ``The Use of Differential Privacy for Census Data and its Impact on Redistricting: The Case of the 2020 U.S. Census.'' Science Advances, Vol. 7, No. 7 (October), pp. 1-17.

Imai, Kosuke and In Song Kim (2021). ``On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel Data.'' Political Analysis, Vol. 29, No. 3 (July), pp. 405-415.

Imai, Kosuke and James Lo (2021). ``Robustness of Empirical Evidence for the Democratic Peace: A Nonparametric Sensitivity Analysis.'' International Organization, Vol. 75, No. 3 (Summer), pp. 901-919.

Imai, Kosuke, Zhichao Jiang, and Anup Malani (2021). ``Causal Inference with Interference and Noncompliance in Two-Stage Randomized Experiments.'' Journal of the American Statistical Association, Vol. 116, No. 534, pp. 632-644.

Imai, Kosuke, and Zhichao Jiang (2020). ``Identification and Sensitivity Analysis of Contagion Effects in Randomized Placebo-Controlled Trials.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 183, No. 4 (October), pp. 1637-1657.

Ning, Yang, Sida Peng, and Kosuke Imai (2020). ``Robust Estimation of Causal Effects via High-Dimensional Covariate Balancing Propensity Score.'' Biometrika, Vol. 107, No. 3 (September), pp. 533-554.

Fifield, Benjamin, Kosuke Imai, Jun Kawahara, and Christopher T. Kenny (2020). ``The Essential Role of Empirical Validation in Legislative Redistricting Simulation.'' Statistics and Public Policy, Vol. 7, No. 1, pp 52-68.

Fifield, Benjamin, Michael Higgins, Kosuke Imai, and Alexander Tarr (2020). ``Automated Redistricting Simulation Using Markov Chain Monte Carlo.'' Journal of Computational and Graphical Statistics, Vol. 29, No. 4, pp. 715-728.

Chou, Winston, Kosuke Imai, and Bryn Rosenfeld (2020). ``Sensitive Survey Questions with Auxiliary Information.'' Sociological Methods & Research, Vol. 49, No. 2 (May), pp. 418-454.

Imai, Kosuke, Gary King, and Carlos Velasco Rivera (2020). ``Do Nonpartisan Programmatic Policies Have Partisan Electoral Effects? Evidence from Two Large Scale Randomized Experiments.'' Journal of Politics, Vol. 82, No. 2 (April), pp. 714-730.

Zhao, Shandong, David A. van Dyk, and Kosuke Imai (2020). ``Propensity-Score Based Methods for Causal Inference in Observational Studies with Non-Binary Treatments.'' Statistical Methods in Medical Research, Vol. 29, No. 3 (March), pp. 709-727.

Lyall, Jason, Yang-Yang Zhou, and Kosuke Imai (2020). ``Can Economic Assistance Shape Combatant Support in Wartime? Experimental Evidence from Afghanistan.'' American Political Science Review, Vol. 114, No. 1 (February), pp. 126-143.

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.

Blair, Graeme, Winston Chou, and Kosuke Imai (2019). ``List Experiments with Measurement Error.'' Political Analysis, Vol. 27, No. 4 (October), pp. 455-480.

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.

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.

Imai, Kosuke and In Song Kim (2019). ``When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data?.'' American Journal of Political Science, Vol. 63, No. 2 (April), pp. 467-490.

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.

Imai, Kosuke, and Zhichao Jiang (2018). ``A Sensitivity Analysis for Missing Outcomes Due to Truncation-by-Death under the Matched-Pairs Design.'' Statistics in Medicine, Vol. 37, No. 20 (September), pp. 2907-2922.

Benjamin, Daniel J., et al (2018). ``Redefine Statistical Significance.'' Nature Human Behaviour. Vol. 2, No. 1, pp. 6-10.

Fong, Christian, Chad Hazlett, and Kosuke Imai (2018). ``Covariate Balancing Propensity Score for a Continuous Treatment: Application to the Efficacy of Political Advertisements.'' Annals of Applied Statistics, Vol. 12, No. 1, pp. 156-177.

Hirose, Kentaro, Kosuke Imai, and Jason Lyall (2017). ``Can Civilian Attitudes Predict Insurgent Violence?: Ideology and Insurgent Tactical Choice in Civil War.'' Journal of Peace Research, Vol. 51, No. 1 (January), pp. 47-63. Winner of the Nils Petter Gleditsch Article of the Year Award. Story by Princeton's communication office.

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.

Rosenfeld, Bryn, Kosuke Imai, and Jacob Shapiro (2016). ``An Empirical Validation Study of Popular Survey Methodologies for Sensitive Questions.'' American Journal of Political Science, Vol. 60, No. 3 (July), pp. 783-802.

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.

Blair, Graeme, Kosuke Imai, and Yang-Yang Zhou (2015). ``Design and Analysis of the Randomized Response Technique.'' Journal of the American Statistical Association, Vol. 110, No. 511 (September), pp. 1304-1319.

Imai, Kosuke and Marc Ratkovic (2015). ``Robust Estimation of Inverse Probability Weights for Marginal Structural Models.'' Journal of the American Statistical Association, Vol. 110, No. 511 (September), pp. 1013-1023. (lead article)

Lyall, Jason, Yuki Shiraito, and Kosuke Imai (2015). ``Coethnic Bias and Wartime Informing.'' Journal of Politics, Vol. 77, No. 3 (July), p. 833-848.

Imai, Kosuke, Bethany Park, and Kenneth Greene (2015). ``Using the Predicted Responses from List Experiments as Explanatory Variables in Regression Models.'' Political Analysis, Vol. 23, No. 2 (Spring), pp. 180-196. Translated in Portuguese and Reprinted in Revista Debates Vol. 9, No 1.

Blair, Graeme, Kosuke Imai, and Jason Lyall (2014). ``Comparing and Combining List and Endorsement Experiments: Evidence from Afghanistan.'' American Journal of Political Science, Vol. 58, No. 4 (October), pp. 1043-1063.

Tingley, Dustin, Teppei Yamamoto, Luke Keele, and Kosuke Imai (2014). ``mediation: R Package for Causal Mediation Analysis.'' Journal of Statistical Software, Vol. 59, No. 5 (August), pp. 1-38.

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.

Lyall, Jason, Graeme Blair, and Kosuke Imai (2013). ``Explaining Support for Combatants during Wartime: A Survey Experiment in Afghanistan.'' American Political Science Review, Vol. 107, No. 4 (November), pp. 679-705. Winner of the Pi Sigma Alpha Award.

Imai, Kosuke and Teppei Yamamoto (2013). ``Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments.'' Political Analysis, Vol. 21, No. 2 (Spring), pp. 141-171. (lead article)

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. Reprinted in Advances in Political Methodology, R. Franzese, Jr. ed., Edward Elger, 2017.

Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto (2013). ``Experimental Designs for Identifying Causal Mechanisms.'' (with discussions) Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 176, No. 1 (January), pp. 5-51. (lead article) Read before the Royal Statistical Society in March, 2012.

Imai, Kosuke, and Dustin Tingley (2012). ``A Statistical Method for Empirical Testing of Competing Theories.'' American Journal of Political Science, Vol. 56, No. 1 (January), pp. 218-236.

Blair, Graeme and Kosuke Imai (2012). ``Statistical Analysis of List Experiments.'' Political Analysis, Vol. 20, No. 1 (Winter), pp. 47-77.

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. Reprinted in Advances in Political Methodology, R. Franzese, Jr. ed., Edward Elger, 2017.

Bullock, Will, Kosuke Imai, and Jacob Shapiro (2011). ``Statistical Analysis of Endorsement Experiments: Measuring Support for Militant Groups in Pakistan.'' Political Analysis, Vol. 19, No. 4 (Autumn), pp. 363-384. (lead article)

Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth Stuart (2011). ``MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.'' Journal of Statistical Software, Vol. 42, No. 8 (Special Volume on Political Methodology), pp. 1-28.

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, 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 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, Luke Keele, and Dustin Tingley (2010). ``A General Approach to Causal Mediation Analysis.'' Psychological Methods, Vol. 15, No. 4 (December), pp. 309-334. (lead article)

Imai, Kosuke, and Teppei Yamamoto (2010). ``Causal Inference with Differential Measurement Error: Nonparametric Identification and Sensitivity Analysis.'' American Journal of Political Science, Vol. 54, No. 2 (April), pp. 543-560.

Imai, Kosuke, Luke Keele, and Teppei Yamamoto (2010). ``Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects.'' Statistical Science, Vol. 25, No. 1 (February), pp. 51-71.

King, Gary, Emmanuela Gakidou, Kosuke Imai, Jason Lakin, Ryan T. Moore, Clayton Nall, Nirmala Ravishankar, Manett Vargas, Martha María Téllez-Rojo, Juan Eugenio Hernández Ávila, Mauricio Hernández Ávila, and Héctor Hernández Llamas (2009). ``Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme.'' (with a comment) The Lancet, Vol. 373, No. 9673 (April), pp. 1447-1454.

Imai, Kosuke (2009). ``Statistical Analysis of Randomized Experiments with Nonignorable Missing Binary Outcomes: An Application to a Voting Experiment.'' Journal of the Royal Statistical Society, Series C (Applied Statistics), Vol. 58, No. 1 (February), pp. 83-104.

Imai, Kosuke, Gary King, and Clayton Nall (2009). ``The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation.'' (with discussions and rejoinder) Statistical Science, Vol. 24, No. 1 (February), pp. 29-53.

Imai, Kosuke, Gary King, and Olivia Lau (2008). ``Toward A Common Framework of Statistical Analysis and Development.'' Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913.

Imai, Kosuke (2008). ``Variance Identification and Efficiency Analysis in Randomized Experiments under the Matched-Pair Design.'' Statistics in Medicine, Vol. 27, No. 24 (October), pp. 4857-4873.

Ho, Daniel E., and Kosuke Imai (2008). ``Estimating Causal Effects of Ballot Order from a Randomized Natural Experiment: California Alphabet Lottery, 1978-2002.'' Public Opinion Quarterly, Vol. 72, No. 2 (Summer), pp. 216-240.

Imai, Kosuke, Gary King, and Elizabeth A. Stuart (2008). ``Misunderstandings among Experimentalists and Observationalists about Causal Inference.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 171, No. 2 (April), pp. 481-502. Reprinted in Field Experiments and their Critics, D. Teele ed. (2014), New Haven: Yale University Press.

Imai, Kosuke (2008). ``Sharp Bounds on the Causal Effects in Randomized Experiments with ``Truncation-by-Death.'' Statistics & Probability Letters, Vol. 78, No. 2 (February), pp. 144-149.

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, and Samir Soneji (2007). ``On the Estimation of Disability-Free Life Expectancy: Sullivan's Method and Its Extension.'' Journal of the American Statistical Association, Vol. 102, No. 480 (December), pp. 1199-1211.

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.

Horiuchi, Yusaku, Kosuke Imai, and Naoko Taniguchi (2007). ``Designing and Analyzing Randomized Experiments: Application to a Japanese Election Survey Experiment.'' American Journal of Political Science, Vol. 51, No. 3 (July), pp. 669-687.

Ho, Daniel E., and Kosuke Imai (2006). ``Randomization Inference with Natural Experiments: An Analysis of Ballot Effects in the 2003 California Recall Election.'' Journal of the American Statistical Association, Vol. 101, No. 475 (September), pp. 888-900.

Imai, Kosuke (2005). ``Do Get-Out-The-Vote Calls Reduce Turnout? The Importance of Statistical Methods for Field Experiments.'' American Political Science Review, Vol. 99, No. 2 (May), pp. 283-300.

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.

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 (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 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, Michael Linzhe Li (2025). ``A Comment on: Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India.'' Econometrica, Vol. 93, No. 4 (July), pp. 1165-1170.

Imai, Kosuke, and Yang Ning (2023). ``Imai, Kosuke, and Yang Ning. (2023). ``Covariate Balancing Propensity Score.'' Handbook of Matching and Weighting Adjust.''

Imai, Kosuke, Michael Rosenblum, and Mark Rothmann (2023). ``14th Annual University of Pennsylvania Conference on statistical issues in clinical trials/subgroup analysis in clinical trials: Opportunities and challenges (afternoon panel discussion).'' Clinical Trials, Vol. 24, No. 4, pp. 405-415.

Imai, Kosuke, Zhichao Jiang, D. James Greiner, Ryan Halen, and Sooahn Shin (2023). ``Authors' Reply to the Discussion of `Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 186, No. 2 (April), pp. 212–216.

Imai, Kosuke (2022). ``Causal Diagrams and Social Science Research.'' Probabilistic and Causal Inference: The Works of Judea Pearl. Geffner, Hector, Rina Dechter, Joseph Y. Halpern, (eds). Association for Computing Machinery and Morgan & Claypool, pp. 647-654.

Imai, Kosuke, and Zhichao Jiang (2019). ``Comment: The Challenges of Multiple Causes.'' Journal of the American Statistical Association, Vol. 114, No. 528, pp. 1605-1610.

de la Cuesta, Brandon and Kosuke Imai (2016). ``Misunderstandings about the Regression Discontinuity Design in the Study of Close Elections.'' Annual Review of Political Science, Vol. 19, pp. 375-396.

Imai, Kosuke (2016). ``Book Review of Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. by Guido W. Imbens and Donald B. Rubin.'' Journal of the American Statistical Association, Vol. 111, No. 515, pp. 1365-1366.

Imai, Kosuke, Bethany Park, and Kenneth F. Greene (2015). ``Usando as respostas previsiveis da abordagem list-experiments como variáveis explicativas em modelos de regressao.'' Revista Debates, Vol. 9, No. 1, pp. 121-151. First printed in Political Analysis, Vol. 23, No. 2 (Spring).

Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto (2014). ``Comment on Pearl: Practical Implications of Theoretical Results for Causal Mediation Analysis.'' Psychological Methods, Vol. 19, No. 4 (December), 482-487.

Imai, Kosuke, Gary King, and Elizabeth A. Stuart (2014). ``Misunderstandings among Experimentalists and Observationalists about Causal Inference.'' in Field Experiments and their Critics: Essays on the Uses and Abuses of Experimentation in the Social Sciences, D. L. Teele ed., New Haven: Yale University Press, pp. 196-227. First printed in Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 171, No. 2 (April).

Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto (2013). ``Reply to Discussions of ``Experimental Designs for Identifying Causal Mechanisms.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 173, No. 1 (January), pp. 46-49.

Imai, Kosuke (2012). ``Comments: Improving Weighting Methods for Causal Mediation Analysis.'' Journal of Research on Educational Effectiveness, Vol. 5, No. 3, pp. 293-295.

Imai, Kosuke (2011). ``Introduction to the Virtual Issue: Past and Future Research Agenda on Causal Inference.'' Political Analysis, Virtual Issue: Causal Inference and Political Methodology.

Imai, Kosuke, Booil Jo, and Elizabeth A. Stuart (2011). ``Commentary: Using Potential Outcomes to Understand Causal Mediation Analysis.'' Multivariate Behavioral Research, Vol. 46, No. 5, pp. 842-854.

Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto (2010). ``Causal Mediation Analysis Using R.'' ,'' in Advances in Social Science Research Using R, ed. H. D. Vinod, New York: Springer (Lecture Notes in Statistics), pp. 129-154.

Imai, Kosuke, Gary King, and Clayton Nall (2009). ``Rejoinder: Matched Pairs and the Future of Cluster-Randomized Experiments.'' Statistical Science, Vol. 24, No. 1 (February), pp. 65-72.

Svyatkovskiy, Alexey, Kosuke Imai, Mary Kroeger, and Yuki Shiraito (2016). ``Large-scale text processing pipeline with Apache Spark.'' IEEE International Conference on Big Data, Washington, DC, pp. 3928-3935.

Goldstein, Daniel, Kosuke Imai, Anja S. Goritz, and Peter M. Gollwitzer (2008). ``Nudging Turnout: Mere Measurement and Implementation Planning of Intentions to Vote.''

Ho, Daniel E. and Kosuke Imai (2004). ``The Impact of Partisan Electoral Regulation: Ballot Effects from the California Alphabet Lottery, 1978-2002.'' ,'' Princeton Law & Public Affairs Paper No. 04-001: Harvard Public Law Working Paper No. 89.

Imai, Kosuke (2003). ``Imai, Kosuke. (2003) Essays on Political Methodology, Ph.D. Thesis. Department of Government, Harvard University. Winner.''

Imai, Kosuke (2003). ``Review of Jeff Gill's Bayesian Methods: A Social and Behavioral Sciences Approach.'' ,'' The Political Methodologist, Vol. 11, No. 1, pp. 9-10.

Imai, Kosuke, and Jeremy Weinstein (2000). ``Measuring the Economic Impact of Civil War.'' ,'' Harvard University Center for International Development, Working Paper Series, No. 51.

Barber, Michael and Kosuke Imai ``Estimating Neighborhood Effects on Turnout from Geocoded Voter Registration Records.''

Chan, K.C.G, K. Imai, S.C.P. Yam, Z. Zhang ``Efficient Nonparametric Estimation of Causal Mediation Effects.''

Hirano, Shigeo, Kosuke Imai, Yuki Shiraito, and Masaki Taniguchi ``Policy Positions in Mixed Member Electoral Systems: Evidence from Japan.''

Imai, Kosuke (2025). ``Nihon wa Data Science Senshinkoku wo Mezase.'' Kagaku, Vol. 95, No. 4. Invited introduction essay for the special issue on data science.

Imai, Kosuke (2022). ``Ippyo no Kakusa: Algorithm de Kaizen Dekiru.'' Nikkei Business, December 19, pp.72-75.

Imai, Kosuke (2007). ``Keiryo Seijigaku niokeru Ingateki Suiron (Causal Inference in Quantitative Political Science).'' Leviathan, Vol. 40, pp. 224-233.

Horiuchi, Yusaku, Kosuke Imai, and Naoko Taniguchi (2005). ``Seisaku Jyoho to Tohyo Sanka: Field Jikken ni yoru Kensyo (Policy Information and Voter Participation: A Field Experiment).'' Nenpo Seijigaku (The Annals of the Japanese Political Science Association), 2005-I, pp. 161-180.

Taniguchi, Naoko, Yusaku Horiuchi, and Kosuke Imai (2004). ``Seito Saito no Etsuran ha Tohyo Kodo ni Eikyo Suruka? (Does Visiting Political Pary Websites Influence Voting Behavior?).'' Nikkei Research Report, Vol. IV, pp. 16-19.