Research
My research sits at the intersection of political behavior, public opinion, experiments, and machine learning methods for social science. Substantively, I study partisanship, polarization, anti-democratic attitudes, and political violence. Methodologically, I work on practical tools for experimental analysis, survey research, and causal inference.
Project links include articles, preprints, code, supplements, and replication materials where publicly available.
Forthcoming
Publications
Attitudes Surrounding Fairness and Competition in Sports Predict Choices to Partisan Gerrymander
Develops a sports-based battery measuring fairness and competitiveness and links those traits to support for partisan gerrymandering.
Affect, Not Ideology: The Heterogeneous Effects of Partisan Cues on Policy Support
Uses causal forests to examine heterogeneity in partisan cue effects and shows how affective attachments shape policy responsiveness.
Populism and the Affective Partisan Space in Nine European Publics
Maps affective party evaluations across European publics and examines how citizens’ affective ratings relate to party populism and ideology.
Assessing the Effectiveness of COVID-19 Vaccine Lotteries
Uses cross-state synthetic control methods to evaluate whether COVID-19 vaccine lottery programs increased vaccination uptake.
The Conditional Effects of Scientific Knowledge & Gender on Support for COVID-19 Government Containment Policies in a Partisan America
Analyzes how scientific knowledge and gender condition support for COVID-19 government containment policies.
Research in Progress
Under Review
What Predicts Support for Political Violence? Results from a Machine Learning Meta-Reanalysis
Analyzes 54 datasets across the social sciences to identify predictors of support for political violence and related attitudes.
The Changing Landscape of Democratic (Dis)Satisfaction: Results from the American National Election Study 1996-2024
Uses ANES data from 1996-2024 to examine changes in democratic satisfaction and dissatisfaction.
Policy or Partisanship? How Polarization Biases Valence Evaluations in the U.S., U.K., and Taiwan
Examines how polarization biases valence evaluations across three political contexts.
Leaving Money on the Table: A Monte-Carlo Study Comparing Causal Forest and Standard Regression Models for Experiments
Compares causal forests and standard regression models for experimental analysis using Monte Carlo simulations.
Are Random Forests Still “Good Enough”? Tabular Prior-Data Fitted Networks for Predictive and Causal Tasks
Evaluates tabular prior-data fitted networks for predictive and causal tasks in social science data.
More Than a Feeling: Theoretical and Empirical Gaps Between Out-Party Affect and Negative Partisanship
Clarifies theoretical and empirical differences between out-party affect and negative partisanship.
Working Papers
The Balance Permutation Test: A Machine Learning Replacement for Balance Tables
Introduces a machine-learning balance test for experimental designs and motivates the accompanying MLbalance R package.