I’m a Ph.D. candidate in Political Science (Comparative Politics) at the University of California, Berkeley, and a Research Associate at the Center of the Politics of Development. Two interconnected themes run through my work: how civilians experience violence, and how they navigate loyalties between state and non-state actors in their search for protection.

My dissertation focuses on an understudied phenomenon in the civil wars literature: legal militias. I ask why civilians form legal militias during civil wars and how legalization shapes the interaction between civilians and armed groups. In particular, I examine how legal militias shape civilian collaboration and violence against civilians.

Methodologically, my work combines quantitative and qualitative approaches to study the politics of violence. I assemble novel datasets and employ research designs that enable me to identify potential causal effects using observational data. I then draw on fieldwork-based evidence to shed light on the mechanisms that may drive those effects. In my dissertation, for example, I assembled a new municipality–year panel dataset of legal militias in Colombia and linked it with data on violence and political institutions, which I analyze using recent advances in panel data methods to identify the causal effects of legalization. I also conducted extensive fieldwork, interviewing national-level decisionmakers such as ministers and peace commissioners, as well as local elites, including large landowners and former militia members.

My work also extends beyond armed groups. In two ongoing collaborations, I examine the effects of feminist protest on violence against women, and I participate in a project that studies youth political participation in smaller cities. This project investigates how young people’s political engagement is shaped by violence and insecurity, an area that has received little attention.

Alongside this substantive research, I contribute to political methodology through the development of the CausalQueries R package. This software enables researchers to specify and update causal models with Bayesian inference in order to answer counterfactual questions within a potential outcomes framework. The package is particularly useful in contexts where causal identification through standard research designs is difficult or impossible. To document and extend this tool, I co-authored the paper Making, Updating, and Querying Causal Models with CausalQueries (with Till Tietz, Georgiy Syunyaev, and Macartan Humphreys), which was recently conditionally accepted at the Journal of Statistical Software.