Kosuke Imai

Professor

Harvard University

United States

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About

Professor Kosuke Imai is a faculty member at Harvard University, USA. His research focuses on experimental design, artificial intelligence applications in decision-making, statistical models of networks, and estimating disparities in social contexts. Recent publications include topics such as biases in experimental designs, AI in presidential campaign advertisements, and evaluating treatment effects using advanced statistical methods.

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Georgetown University

UNITED STATES

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Kosuke Imai

Research Interests

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About

Articles (27)

Priming Bias Versus Post-Treatment Bias in Experimental Designs

Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952–2012

Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies

A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate

Estimating Racial Disparities When Race is Not Observed

Bayesian safe policy learning with chance constrained optimization: application to military security assessment during the Vietnam War

Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

Estimating Average Treatment Effects With Support Vector Machines

Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

Rejoinder: We Can Improve the Usability of the Census Noisy Measurements File

Evaluating bias and noise induced by the U.S. Census Bureau’s privacy protection methods

Census officials must constructively engage with independent evaluations

Policy Learning with Asymmetric Counterfactual Utilities

Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Measuring and Modeling Neighborhoods

A summer bridge program for first-generation low-income students stretches academic ambitions with no adverse impacts on first-year GPA

Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation Study

Statistical inference and power analysis for direct and spillover effects in two‐stage randomized experiments

14th Annual University of Pennsylvania Conference on statistical issues in clinical trials/subgroup analysis in clinical trials: Opportunities and challenges (afternoon panel discussion)

Widespread partisan gerrymandering mostly cancels nationally, but reduces electoral competition

Race and ethnicity data for first, middle, and surnames

Authors' reply to the Discussion of ‘Experimental evaluation of algorithm-assisted human decision-making: application to pretrial public safety assessment.’

Experimental evaluation of algorithm-assisted human decision-making: application to pretrial public safety assessment*

Keyword‐Assisted Topic Models

Comment: The Essential Role of Policy Evaluation for the 2020 Census DisclosureAvoidance System

Experimental Evaluation of Individualized Treatment Rules

Making Differential Privacy Work for Census Data Users

Collaborators (8)

Shiro Kuriwaki

Matthew Blackwell

Anup Malani

Cory McCartan

Michael Lingzhi Li

Jacob Brown

Eli Ben-Michael

Rebecca Johnson

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