News & Updates
My paper with Hilary Izatt, “Attitudes Surrounding Fairness and Competition in Sports Predict Choices to Partisan Gerrymander,” received an R&R at Political Behavior. It leverages scaling methods to uncover latent dimensions surrounding fairness and looks at how this relates to support for in-party gerrymandering.
My paper with Nicolás de la Cerda and Jack T. Rametta, “Affect, Not Ideology: The Heterogeneous Effects of Partisan Cues on Policy Support,” is now forthcoming at Political Behavior. This paper leverages Causal Forest to explore the conditional effects of partisan cues (as you may have guessed, affect matters but ideology doesn’t).
My frequent collaborator, Jack T. Rametta, and I will be teaching an ICPSR Summer Program Topical Workshop titled “Causal Machine Learning for Observational and Experimental Research”. If you’re interested in our CML work, definitely check this class out!
Relatedly, our working paper “Causal Forest and Doubly Robust Machine Learning for Political Science” is now available on OSF.
Sam Fuller
Postdoctoral Fellow
Machine Learning & Experiments
Polarization & Partisanship
Political Violence
About Me
Hi there! I’m a Postdoctoral Fellow in Public Opinion and Survey Methodology at the Center for American Political Studies in the Department of Government at Harvard University.
My methodological research is primarily focused on creating new, and applying existing, machine learning methods for the analysis of experimental and survey data. Importantly, I care deeply about bridging the gap between methodologists who create theses methods and substantive scholars who would benefit greatly from using these methods. In that vein, I’m currently working on a series of projects with Jack T. Rametta that introduces standardized, easy-to-use ML methods for analyzing experimental data. Our first methodological and substantive papers in this series are currently under review and available here and here on OSF, respectively. I’m more than happy to field any questions and I’m definitely interested in receiving useful feedback!
My substantive research focuses on, first, improving our measurement and, second, increasing our understanding of phenomenon in public opinion, political behavior, and political psychology. Particularly, I study ideological and affective polarization as well as partisanship as an identity and am looking at its affects on policy preferences and anti-democratic attitudes. Additionally, I am also interested in a wide-array of social science questions, including those in Sociology. I am currently working on a project with Professor Caitlin Patler that leverages machine learning (specifically, Doubly Robust Machine Learning (DRML)) to estimate the causal effects of immigration detention/incarceration on mental and physical health.
I have published in journals such as PLOS One, Social Science Quarterly, Politics & Gender, and Frontiers in Political Science. My previous work focuses primarily on COVID-19 attitudes, behaviors, and policy outcomes. I also have a paper on leveraging multidimensional scaling to measure ideology and populism in Western Europe (I think it’s a neat paper, check it out!).
Research interests
Machine Learning | Experiments | Political Behavior | Affective Polarization
Education
🎓 PhD in Political Science | University of California, Davis
🎓 BS in Political Science & Economics | Berry College