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 Downfall of the Legislative State: Congressional Capacity before and after the Republican Revolution

My dissertation focuses on Congressional oversight and policymaking capacity, as well as inter-branch politics. To this end, I compile two novel datasets encompassing the universe of public reports from the Congressional Research Service and the Government Accountability Office. With these datasets, I am able to derive high resolution images of the effects of the Contract with America on Congressional oversight and policymaking capacity across a wide range of substantive domains and federal agencies. These data also provide a venue to test longstanding theories of inter-branch and legislative-bureaucratic relations.

Book Project

Research under Review

Selected Research in Preparation

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

  • The Dangers of Calculating Conditional Effects: A Reevaluation of Barber and Pope (2019) (with Sam Fuller). Presented at MPSA 2024.

  • How Robust are Subgroup Analyses in Political Science? Insights from Dozens of Replications (with Giulia Venturini, Richard L. Kornrumpf, and Sam Fuller)

  • Why Play by the Rules? Legislative Delegation to Scorekeepers in the Context of Electoral Competition