Yuki Atsusaka

Welcome! I am an incoming Assistant Professor at the Hobby School of Public Affairs (University of Houston) and I hold a PhD in Political Science and MA in Statistics from Rice University. I also hold an MA in American Studies (with a focus on African American History and Culture) from Doshisha University.

In 2022-2023, I serve as a Guarini Dean’s Fellow in the Politics of Race and Ethnicity at Dartmouth College. My research focuses on electoral systems, race and ethnic politics, and political methodology. More specifically, I develop and apply new quantitative methods to study the effects of electoral engineering on minority representation in racially and ethnically diverse democracies. My dissertation titled Political Methodologies for Electoral Engineering and Minority Representation received the John W. Gardner Award for the Best Dissertation in Social Sciences.

Substantive focus: ranked-choice voting, minority representation
Methodological focus: ranking data, logical models

Email: Yuki.Atsusaka@dartmouth.edu
Google Scholar: YukiAtsusaka
Dataverse: YukiAtsusaka
Twitter: @Yuki_Atsusaka
Curriculum Vitae: pdf
GitHub: here


February 16th, 2023
I am hosting a workshop titled “Diversity, Equity, and Inclusion and Political Science: A Roundtable” at Dartmouth College. See you all soon! [Slides] [More info, registration]

February 1st & 3rd, 2023
On 2/1, I presented my talk “Quantitatively Predictive Logical Models” in the political methodology workshop. Many thanks to Silvia Kim and Jeff Gill. [Slides] On 2/3, I talked about my paper “Causal Inference with Ranking Data” in the department talk series. Big thanks to Andy Ballard and Laura Paler! Both at American University. [Slides]
November 30th, 2022
I presented my paper “Causal Inference with Ranking Data” at the Junior Americanist Workshop Series (JAWS). Thanks to Justin Grimmer (discussant) and the participants for their valuable feedback and comments! [Slides]
September 13th, 2022
I launched a daily tweet challenge on ranking data. Join me here to learn more about ranking and ranking data in political science research!
June 1st, 2022
I started my new position as a postdoctoral fellow at Dartmouth College.


2. Yuki Atsusaka and Randolph T. Stevenson. 2023. A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents Political Analysis 31 (1) 134-148. [R Package: cWise] [Article Summary] [Replication Files] [Preprint]

1. Yuki Atsusaka. 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

2. Yuki Atsusaka. “Causal Inference with Ranking Data: Application to Blame Attribution in Police Violence and Ballot Order Effects in Ranked-Choice VotingUnder Review.

1.Yuki Atsusaka and Theodore Landsman. “Does Ranked-Choice Voting Reduce Racial Polarization? Evidence from Agent-Based Modeling and Bay Area Mayoral Elections(A winner of New America’s Electoral Reform Research Group Grant)

Selected Current Works

Research Program 1: Ranking Data
“Missing Data Approach to Partially Ranked Data: Application to Polarization in Ranked-Choice Voting”
“Addressing Non-Sincere Responses in Rank-Order Survey Questions in Social Sciences” (with Seo-young Silvia Kim)
“Conjoint Experiments with Multiple Profiles: Application to Minority Representation in Ranked-Choice Voting” (with Yusaku Horiuchi)

Research Program 2: Logical Models

“Making Predictions Without a Black Box: Intra-Party Vote Shares in Open-List Proportional Representation” (with Francisco Cantu)
“Predicting Racial Minority Representation across Electoral Systems with Logical Models”
“Theoretical Foundations for Evaluating Minority Representation”


  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 graduate courses such as Causal Inference (Fall 2019), Advanced Maximum Likelihood Estimation (Spring 2019), and Machine Learning/Computational Social Science (Fall 2020) and undergraduate classes such as Applied Research Methods (2021-2022). I have also founded and organized the Rice Methodology Research Group in the Department of Political Science.