Kosuke Imai

About

Professor Kosuke Imai advises a large number of graduate students across various subfields and disciplines, including statisticians, methodologists, and those focused on substantive research areas. He participates actively in dissertation committees and regularly engages with students through weekly research group meetings where he provides guidance on dissertation research. Professor Imai collaborates with graduate students on research projects, emphasizing the importance of strong training in statistics and computational skills, as well as independent thinking and initiative. He writes recommendation letters for both graduate and undergraduate students, prioritizing efficiency and requiring detailed application materials and a waiver of the right to access the letter. Professor Imai is committed to supporting students once they are admitted to PhD programs and encourages early communication regarding dissertation committee participation and research collaboration.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Data Mining
• Statistics
• Sociology
• Machine Learning
• Political Science
• Econometrics
• Law

Selected publications

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

  Political Analysis · 2020 · 573 citations

  1st authorCorresponding

  Abstract The two-way linear fixed effects regression ( 2FE ) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unobserved unit-specific and time-specific confounders at the same time. Unfortunately, we demonstrate that the ability of the 2FE model to simultaneously adjust for these two types of unobserved confounders critically relies upon the assumption of linear additive effects. Another common justification…

  DOI

• Matching Methods for Causal Inference with Time‐Series Cross‐Sectional Data

  American Journal of Political Science · 2021 · 323 citations

  1st authorCorresponding

  Abstract Matching methods improve the validity of causal inference by reducing model dependence and offering intuitive diagnostics. Although they have become a part of the standard tool kit across disciplines, matching methods are rarely used when analysing time‐series cross‐sectional data. We fill this methodological gap. In the proposed approach, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history u…

  DOI

• The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census

  Science Advances · 2021 · 107 citations

  Senior authorCorresponding

  Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureau’s latest disclosure avoidance sy…

  DOI

• Automated Redistricting Simulation Using Markov Chain Monte Carlo

  Journal of Computational and Graphical Statistics · 2020 · 84 citations

  Legislative redistricting is a critical element of representative democracy. A number of political scientists have used simulation methods to sample redistricting plans under various constraints to assess their impact on partisanship and other aspects of representation. However, while many optimization algorithms have been proposed, surprisingly few simulation methods exist in the published scholarship. Furthermore, the standard algorithm has no theoretical justification, scales poorly, and is u…

  DOI

• Widespread partisan gerrymandering mostly cancels nationally, but reduces electoral competition

  Proceedings of the National Academy of Sciences · 2023 · 48 citations

  Senior authorCorresponding

  Congressional district lines in many US states are drawn by partisan actors, raising concerns about gerrymandering. To separate the partisan effects of redistricting from the effects of other factors including geography and redistricting rules, we compare possible party compositions of the US House under the enacted plan to those under a set of alternative simulated plans that serve as a nonpartisan baseline. We find that partisan gerrymandering is widespread in the 2020 redistricting cycle, but…

  PublisherOA PDFDOI

Recent grants

• Statistical Analysis of Causal Mechanisms: Identification, Inference, and Sensitivity Analysis

  NSF · $98k · 2009–2012

• Collaborative Research: Generalized Propensity Score Methods

  NSF · $120k · 2006–2009

• Evaluating the Impacts of Machine Learning Algorithms on Human Decisions

  NSF · $330k · 2021–2024

Frequent coauthors

• Brandon De La Cuesta

  Stanford University

  1065 shared

• Naoki Egami

  Columbia University

  1064 shared

• Christopher T Kenny

  206 shared

• Benjamin Fifield

  Quantitative BioSciences

  205 shared

• Jun Kawahara

  187 shared

• In Song Kim

  131 shared

• Steven Liao

  Providence College

  111 shared

• Gary King

  Harvard University Press

  95 shared

Labs

• Sports Analytics Laboratory at Harvard UniversityPI

  The Sports Analytics Laboratory at Harvard University focuses on the application of statistical and computational methods to sports.

Education

• Ph.D., Statistics

  Harvard University

  2006

• M.A., Statistics

  Harvard University

  2003

• B.A., Mathematics

  University of Tokyo

  1999

Similar researchers at Harvard University

• Susan Murphyh-188
• Donald B. Rubinh-151
• Giovanni Parmigianih-141