Sam Fuller
Postdoctoral Fellow
Lecturer in Political Methodology Machine Learning & Experiments
Political Violence & Democracy
Democracy & Political Violence Collaborative
Co-founded with Gal Bitton and Jack T. Rametta, we hosted our first meeting as an APSA Pre-Conference on 9/2/26 at Harvard University.
Partisanship, (Anti)Democratic Attitudes, & Support for Political Violence
My substantive research examines partisanship as a social identity and its relationship to democratic satisfaction, anti-democratic attitudes, partisan cues, gerrymandering, and political violence.
Causal Machine Learning
I work with Jack T. Rametta on numerous causal machine learning projects, including teaching a course and writing a book (along with Christopher D. Hare).
I am a political scientist studying how partisanship, identity, and political conflict shape anti-democratic attitudes and support for political violence. Methodologically, I work on machine learning tools for experiments and surveys, with an emphasis on making flexible models useful for substantive researchers.
The Politics of Supporting Violence
My book project, The Politics of Supporting Violence, coauthored with Jack T. Rametta, Gal Bitton, and Alexa Federice aims to define what support for political violence is and why it is distinctly important from perpetration in its implications for democracy. We now have two papers resulting from our work on this book, with a third on interventions directed at reducing SPV in the pilot stage.
News & Updates
I am serving as Lecturer in Political Methodology at Harvard University during the 2026 school year, teaching the undergraduate methods sequence and a PhD course on machine learning.
Jack T. Rametta and I taught our 2026 ICPSR Summer Program Topical Workshop, “Causal Machine Learning for Observational and Experimental Research”.
About Me
I am a Postdoctoral Fellow in Public Opinion and Survey Methodology at the Center for American Political Studies in the Department of Government at Harvard University, and a Lecturer in Political Methodology in Harvard’s Department of Government.
I also direct the Harvard Digital Labs for the Social Sciences and serve as Academic Lead for the Harvard CAPS-Harris Poll.
My substantive research focuses on the relationship between partisanship as a social identity and anti-democratic attitudes, including gerrymandering and support for political violence. Much of my work here focuses on leveraging measurement and scaling to better capture both the structure of partisanship and these anti-democratic attitudes. I am also working with Professor Caitlin Patler on a project that uses Causal Machine Learning to estimate the causal effects of immigration detention/incarceration on mental and physical health.
My methodological research is focused on creating new, and applying existing, machine learning methods for the analysis of experimental and survey data. I care deeply about bridging the gap between methodologists who create these methods and substantive scholars who would benefit from using them. In that vein, I am working on a series of projects with Jack T. Rametta that introduces standardized, easy-to-use ML methods for analyzing experimental data.
I have published in journals such as Political Behavior, PLOS One, Social Science Quarterly, Politics & Gender, and Frontiers in Political Science. My previous work focuses on COVID-19 attitudes, behaviors, and policy outcomes, as well as multidimensional scaling approaches to measuring ideology and populism in Western Europe.
Education
PhD in Political Science | University of California, Davis
BS in Political Science & Economics | Berry College