World's Best Scientists 2026 revealed!

D-Index & Metrics

Social Sciences and Humanities

D-Index
59
Citations
42716
World Ranking
1504
National Ranking
717

Overview

Kosuke Imai is affiliated with Harvard University in the United States. Their research spans several interconnected fields, focusing predominantly on statistical methods, causal inference, and political science.

The scientist has made notable contributions in various research areas, including:

  • Advanced Causal Inference Techniques
  • Judicial and Constitutional Studies
  • Statistical Methods and Inference
  • Census and Population Estimation
  • Electoral Systems and Political Participation
  • Healthcare Policy and Management
  • Statistical Methods and Bayesian Inference

In terms of disciplinary focus, Kosuke Imai's work bridges several subfields, notably:

  • Statistics and Probability
  • Economics and Econometrics
  • Sociology and Political Science
  • Law
  • Artificial Intelligence

Their recent publications include research articles that address methodological issues in political analysis and causal inference. Some key papers are:

  • "On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data" (2020), published in Political Analysis
  • "Matching Methods for Causal Inference with Time-Series Cross-Sectional Data" (2021), published in American Journal of Political Science
  • "Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution" (2021), published in Political Analysis
  • "The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census" (2021), published in Science Advances
  • "Keyword-Assisted Topic Models" (2023), published in American Journal of Political Science

Kosuke Imai frequently collaborates with researchers such as Brandon De La Cuesta, Naoki Egami, Christopher T Kenny, Benjamin Fifield, and Jun Kawahara, indicating a strong network in social science methodology and statistical research.

The scientist has disseminated work in prominent publication venues, with multiple contributions to:

  • Harvard Dataverse
  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Political Analysis
  • Journal of the American Statistical Association

Best Publications

  • Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

    Daniel E. Ho;Kosuke Imai;Gary King;Elizabeth A. Stuart

  • MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

    Daniel E. Ho;Kosuke Imai;Gary King;Elizabeth A. Stuart

  • A general approach to causal mediation analysis.

    Kosuke Imai;Luke Keele;Dustin Tingley

  • mediation: R Package for Causal Mediation Analysis

    Dustin Tingley;Teppei Yamamoto;Kentaro Hirose;Luke Keele

  • Redefine statistical significance

    Daniel J. Benjamin;James O. Berger;Magnus Johannesson;Magnus Johannesson;Brian A. Nosek;Brian A. Nosek

  • Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects

    Kosuke Imai;Luke Keele;Teppei Yamamoto

  • Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies

    Kosuke Imai;Luke Keele;Dustin Tingley;Teppei Yamamoto

  • Covariate balancing propensity score.

    Kosuke Imai;Marc Ratkovic

  • Misunderstandings between experimentalists and observationalists about causal inference

    Kosuke Imai;Gary King;Elizabeth A. Stuart

  • Causal Inference with General Treatment Regimes: Generalizing the Propensity Score

    Kosuke Imai;David A. van Dyk

  • Statistical Analysis of List Experiments

    Graeme Blair;Kosuke Imai

  • Explaining Support for Combatants during Wartime: A Survey Experiment in Afghanistan

    Jason Lyall;Graeme Blair;Kosuke Imai

  • Estimating treatment effect heterogeneity in randomized program evaluation

    Kosuke Imai;Marc Ratkovic

  • On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data

    Kosuke Imai;In Song Kim

  • Experimental designs for identifying causal mechanisms

    Kosuke Imai;Dustin Tingley;Teppei Yamamoto

  • Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme

    Gary King;Emmanuela Gakidou;Kosuke Imai;Jason Lakin

  • Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments

    Kosuke Imai;Teppei Yamamoto

  • Multivariate Regression Analysis for the Item Count Technique

    Kosuke Imai

  • Causal Mediation Analysis Using R

    Kosuke Imai;Luke Keele;Dustin Tingley;Teppei Yamamoto

  • The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance Evaluation

    Kosuke Imai;Gary King;Clayton Matthew Nall

  • A Bayesian analysis of the multinomial probit model using marginal data augmentation

    Kosuke Imai;David A. van Dyk

  • Experimental Designs for Identifying Causal Mechanisms (with discussions)

    Kosuke Imai;Dustin Tingley;Teppei Yamamoto

Frequent Co-Authors

Gary King
Gary King Harvard University
Dustin Tingley
Dustin Tingley Harvard University
Luke Keele
Luke Keele University of Pennsylvania
Elizabeth A. Stuart
Elizabeth A. Stuart Johns Hopkins University
David Cesarini
David Cesarini New York University
Thomas D. Cook
Thomas D. Cook University of Wisconsin–Madison
Brian A. Nosek
Brian A. Nosek Center for Open Science
Andy P. Field
Andy P. Field University of Sussex
Anthony G. Greenwald
Anthony G. Greenwald University of Washington
Zoltan Dienes
Zoltan Dienes University of Sussex

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