Yuki Atsusaka

Hello! I am a Ph.D. Candidate (political science) and M.A. student (statistics) at Rice University in Houston, Texas (currently on the academic job market). Thank you for visiting my website!

I study political methodology and American-Comparative politics. Substantively, I am studying how electoral engineering affects political representation in American politics. Methodologically, I am interested in developing statistical and mathematical tools that can advance our research in racial and ethnic politics, minority representation, and electoral engineering. My current works study how rank data analysis (as part of computational social science) and quantitatively predictive logical models can advance political science research and legal debates about minority representation.
Rank Data Analysis for Social Sciences (sample syllabus, forthcoming)
Quantitatively Predictive Logical Models (sample syllabus, forthcoming)

I draw many ideas and tools from Comparative Politics and Statistics to study American Politics. My Curriculum Vitae is available here. I am on Twitter too!


2. Atsusaka, Yuki and Randolph T. Stevenson. 2021. “A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents” Conditionally Accepted by Political Analysis [R Package: cWise] [Article Summary]

1. Atsusaka, Yuki. 2021. “A Logical Model for Predicting Minority Representation: Application to Redistricting and Voting Rights Cases” American Political Science Review (Publisher’s Version: First View) [R Package: logical] [Article Summary]

Working Papers

Survey Methodology
“Diagnostic Tools for the Crosswise Model: Synthesizing Application and Validation Studies”

American Politics
“A Unified Theory of the Effect of Vote-by-Mail on Turnout” (with Robert M. Stein) Under Review

Logical Models
“A Logical Model Approach to Racially Polarized Voting, Descriptive Representation, and Electoral Engineering” (To be presented at PolMeth and APSA)
“Reconsidering the Effect of At-Large Elections on Minority Representation” (with Iris E. Acquarone)

Rank Data
“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)
“Causal Inference with Rankings as Generalized Discrete Outcomes” (Dissertation Paper)

“An Ecological Inference Model with Rank Data for Social Sciences” (with Thomas Weighill)
“Ranked Preferences on National Policy, Pork Barrel, and Casework by Race” (with Matthew Hayes)
“A Framework for Rank Data Analysis in Political Science”


In my dissertation, I develop statistical methods for analyzing rank data. Substantively, my project examines the consequences of an emerging electoral reform — switching from plurality rule to ranked-choice voting — on voters’ preferences and representation in the U.S. My dissertation is supported by the Electoral Reform Research Group (New America).

In “Statistical Methods for Partially Ranked Ballot Data,” I propose a new methodological framework for analyzing partially ranked data with strategic rank concentration. The proposed approach enables researchers to explain and predict the nature and variation of partial rankings within and across rank data. I also provide new graphical representations of partial rankings, a hypothesis test for strategic rank concentration, statistical models for rank aggregation and clustering in the presence of plumping, a strategy to include individual and aggregate covariates, and a practical guideline for facilitating substantive interpretation. I illustrate the proposed methodology by applying it to data from over 200 ranked-choice voting (RCV) elections in the U.S., attempting to resolve a controversy over the nature, size, and trend of partially ranked ballots in RCV elections in American politics.

In “Causal Inference with Rankings as Generalized Discrete Outcomes,” I propose a potential outcomes-based framework for identifying and estimating causal effects of treatments on ranked outcomes in randomized controlled trials. With this framework, researchers can make causal inferences on ranked preferences of individuals on a wide array of items in social sciences and medical science. Given the high-dimensional nature of ranked outcomes, I introduce three different causal estimands and derive appropriate estimators (both for partial and point identification) and inference methods for them. I present several optimal experimental designs for each estimand and discuss potential issues that applied researchers must be aware of. I illustrate the proposed methodology with an empirical example.

In “Does Ranked-Choice Voting Reduce Racially Polarized Voting?” (with Theo Landsman), I introduce a neo-Downsian spatial model for ethnic party competition under First-Past-the-Post (FPTP) and ranked-choice voting (RCV) elections. I also offer empirical evidence for the moderation effect of the RCV implementation on the degree of racially polarized voting. To provide a valid causal effect of switching from FPTP to RCV elections on racial polarization, I offer a novel measurement of racially polarized voting that is comparable across the electoral systems based on rank clustering methods and ecological inference. Moreover, I collect precinct returns and cast vote records from Bay Area mayoral elections from 1994 to 2020. With the measurement of racial polarization, I estimate the causal effect of the RCV implementation by dealing with structural missing values (no elections) in the collected time-series cross-sectional data.


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


As a solo-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 am also an organizer of the Methodology Research Group in the Department of Political Science.


I am a Fulbright scholar since 2016 and a founder of SHIPS (Seminar for Hiphop Studies), a study and social group on Black culture in Kyoto, Japan. Before moving to Houston, I studied African American History and Culture and Hiphop in an M.A. program (American Studies) at Doshisha University in Kyoto, Japan. I still got the vision like a line between two dots ÷ !

I will be presenting my works at the PolMeth on July, 2021, and APSA on September, 2021. Looking forward to seeing you there!