Join us for a talk by Prof. Asya Magazinnik on causal model and their application in making data-informed counterfactual claims about interventions.
Abstract from speaker:
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 chapter of our book project, we 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. The chapter proceeds as follows. First, we remind the reader of the basic structure of the Neyman-Rubin causal model, and we explain how this framework has been applied to preference elicitation experiments. Then, we 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.
Bring your own lunch bag! Light pastries and drinks will be available in case you forget to bring it.
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The Data Science Brown Bag Series is an informal and interactive gathering where participants bring their own brown bag lunch and engage in discussions on research and insights the field of data and computational social science (light pastries and drinks will be available if you forget your lunch bag!).
The series provides a platform for data enthusiasts, researchers, and practitioners to share their experiences, best practices, and emerging methodologies and research in using data science to analyze and understand social and political phenomena. The brown bag talk series is for anyone interested in data science and social science to network, learn, and share ideas in a casual and friendly setting.
