Research

Research Interests

Substantive: American political institutions, Congressional policymaking & oversight capacity, legislative support agencies, political behavior, ideology and public policy
Methods: causal inference and causal machine learning, Monte Carlo methods, predictive modeling, design-based inference, experiments, measurement, game theory, text analysis

Affect, Not Ideology: The Heterogeneous Effects of Political Cues on Policy Support. Coauthored with Nicolás de la Cerda and Sam Fuller. 2025 Political Behavior. Presented at WPSA 2023.

Advanced Machine Learning for Experiments in the Social Sciences. Coauthored with Christopher D. Hare and Sam Fuller. Advance Contract, Cambridge Elements: Experimental Political Science. Expected 2026-2027.

Did the Republican Revolution Hamstring Congressional Oversight? Evidence from 55,000 GAO Reports. Under Review. Presented at MPSA 2024.
The Balance Permutation Test: A Machine Learning Replacement for Balance Tables (with Sam Fuller). Under Review. Presented at UC Davis Political Science Research Workshop and ICPSR 2024.
What Predicts Support for Political Violence? Results from a Machine Learning Meta-Reanalysis (with Sam Fuller and Alexa Federice). Under Review. Presented at Harvard American Politics Research Workshop 2024, MPSA 2025, and APSA 2025
Causal Forest and Double Machine Learning for Political Science (with Sam Fuller). Presented at MPSA 2023, APSA 2024

Populism and the Political Economy of Congressional Professionalization
Rational Voting in the Age of Ideological Polarization & Responsible Parties: Examining Presidential Elections from 1972–2024. (with Carlos Algara and Sam Fuller).
Are Random Forests “Good Enough”? Exploring the Utility of Prior-Data Fitted Networks for Predictive and Causal Tasks
Fast and Improved (Ordered) Optimal Classification (with Christopher D. Hare).
The Dangers of Calculating Conditional Effects: A Reevaluation of Barber and Pope (2019) (with Sam Fuller). Presented at MPSA 2024.