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, game theory, text analysis

Dissertation: The Legislative State and the Republican Revolution: A Causal Machine Learning Approach

In my dissertation research, I interrogate the effects of the Republican Revolution of 1994 on Congressional oversight capacity using a combination of traditional causal inference methods and causal machine learning approaches. The venue for this investigation is a novel dataset that comprises the universe of published and publicly available material from the Government Accountability Office (GAO), Congress’ understudied “watchdog” oversight and auditing agency. Through my investigation I reveal the deleterious effects of the Republican Revolution on oversight capacity at GAO. In the first chapter, I explore the effects of the Republican Revolution in terms of several different dependent variables of interest. In the second chapter, I explore heterogeneity in these main effects using a novel causal machine learning estimation procedure. Finally, in the third chapter, I explore the broader applicability of causal machine learning methods in the social sciences in light of recent debates. My results contribute to a growing literature that is skeptical of Congress’ ability to effectively oversee the executive branch. This dissertation also contributes to a growing literature that applies blackbox predictive algorithms for inference.

Publications

• 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.

Book Project

• 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 2025-2026.

Research under Review

• 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.

• Causal Forest and Double Machine Learning for Political Science (with Sam Fuller). Presented at MPSA 2023, APSA 2024

Selected Research in Preparation

• Populism and the Political Economy of Congressional Professionalization

• Something’s Wrong with the Kids: The Ubiquitous Relationship between Youth and Support for Political Violence (with Sam Fuller and Alexa Federice). Presented at Harvard American Politics Research Workshop 2024, MPSA 2025, and APSA 2025

• One King to Rule Them All? TabPFNs for Predictive and Causal Tasks in the Social Sciences

• 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.