Yuki Atsusaka

WELCOME! I am a Ph.D. (political science) and M.A. (statistics) Candidate at Rice University (contact: atsusaka[at]rice.edu). Thank you for visiting my website.

Broadly, I study race and ethnic politics, electoral systems, redistricting and voting rights, and political methodology. More specifically, I develop and apply quantitative methods to study how to design electoral institutions to achieve racially fair representation in racially diverse democracies, including the United States. My current works study how to use rank data analysis (as part of computational social science) and quantitatively predictive logical models to advance research on and legal debates about racial minority representation. My dissertation develops new methods to study the effects of ranked-choice voting on racial minority representation.

My Curriculum Vitae is available here. Follow me on Twitter!


2. Atsusaka, Yuki and Randolph T. Stevenson. 2021. A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents Political Analysis (FirstView) [R Package: cWise] [Article Summary] [Replication Files] [Preprint]

1. Atsusaka, Yuki. 2021. A Logical Model for Predicting Minority Representation: Application to Redistricting and Voting Rights Cases American Political Science Review 115 (4), 1210-1225. [R Package: logical] [Article Summary] [Replication Files]

Working Papers

Quantitatively Predictive Logical Models
Theoretical Foundations for Evaluating Minority Representation

Rank Data Analysis
Does Ranked-Choice Voting Reduce Racial Polarization? (with Theo Landsman) [Media Coverage] (Dissertation Paper)A winner of New America’s Electoral Reform Research Group Grant
“Statistical Methods for Partially Ranked Ballot Data” (Dissertation Paper)
“An Ecological Inference Model with Rank Data for Social Sciences” (with Thomas Weighill)
“Ranked Preferences over National Policy, Pork Barrel, and Casework by Race” (with Matthew Hayes)

Causal Inference/Program Evaluation
“Causal Inference with Rankings as Generalized Discrete Outcomes” (Dissertation Paper)
A Unified Theory of the Effect of Vote-by-Mail on Turnout (with Robert M. Stein)


In my dissertation, I develop statistical methods for analyzing rank data. Substantively, my project examines the consequences of an emerging electoral reform — switching from first-past-the-post to ranked-choice voting — for minority representation in the U.S. My dissertation is supported by the Electoral Reform Research Group (New America) and is composed of following papers:

“Causal Inference with Rankings as Generalized Discrete Outcomes”
“Statistical Methods for Partially Ranked Ballot Data”
“Does Ranked-Choice Voting Reduce Racially Polarized Voting?” (with Theo Landsman)

More information is available here.


  1. cWise: A (Cross)Wise Method to Analyze Sensitive Survey Questions
  2. logical: Computing and Visualizing Quantitative Predictions of Logical Models


As an instructor, I have taught the Math Prep (aka Math Prefresher) for the first-year graduate students in 2018-2019, Social Analysis and Simulation in R for the second-year graduate students in 2019, and Ecological Inference in Advanced Political Methodology in 2019. In addition, I have worked as a teaching assistant for Causal Inference (Michelle Torres, Fall 2019), Advanced Maximum Likelihood Estimation (Randy Stevenson, Spring 2019), and Machine Learning/Computational Social Science (Michelle Torres, Fall 2020). I have also founded and organized the Rice Methodology Research Group in the Department of Political Science.


I am a Fulbright scholar from 2016 to 2021. I also hold an M.A. in American Studies with a focus on African American History and Culture.