Asya Magazinnik a.magazinnik@hertie-school.org
Version: MZES Social Science Data Lab, 2024-02-21
The Neyman-Rubin causal model characterizes how, through experimental (or quasi-experimental) manipulation of an intervention, researchers can make data-informed counterfactual claims about what would happen in the absence of that intervention. The Neyman-Rubin causal model is, nevertheless, just that: a model. In this talk, I will present some excerpts from a larger book project in which my collaborators and I describe the connections between the Neyman-Rubin causal model, the basic estimands of randomized control trials targeting respondents’ preferences, and the theoretical object that is traditionally described as a preference. After a brief reminder of the basic structure of the Neyman-Rubin causal model, I will explain how this framework has been applied to preference elicitation experiments. Then, I will proceed to show that, although this gives us well-defined counterfactuals, the corresponding causal quantities do not straightforwardly represent preferences, either at the individual level or in the aggregate. Finally, I will present a model-based alternative for preference elicitation, with a hands-on application using replication data from a survey experiment.
📝 Slides
Asya Magazinnik is Professor of Social Data Science at the Hertie School. Her research interests include electoral geography, federalism, local politics, and law enforcement. She also works on political methodology, in particular at the intersection of causal inference and formal theory. Her work has appeared in the American Journal of Political Science, the Journal of Politics, and other outlets. Previously, she was an Assistant Professor of Political Science at MIT. She earned a PhD in Political Science from Princeton University in 2020 and holds an MPP from the Harris School at the University of Chicago.