Meeting Link: https://umich.zoom.us/j/93338242486
Passcode: csaaw2021
Phone ID: 933 3824 2486
Phone Passcode: 400052931

Abstract: Many datasets in social sciences are a result of agents making repeated choices over time, with some observable outcome resulting from each choice. Researchers often want to model the causal impact of covariates on the outcome variable using different estimation strategies (e.g. fixed effects regression, difference-in-differences, instrumental variables, etc). I propose a way to increase control in these estimation procedures by using network theory models motivated by a discrete choice framework. I suggest a bi-partite network representation of these datasets, with agents being nodes on one side of the network and choices being nodes on the other side of it. Edges in this network represent a choice made by an agent at a certain time, resulting from a discrete choice problem. I argue that the structure of connections in this choice-network allows the researcher to further improve controls when modelling the outcome variable. For instance, I use the choice-network to project agents in a multidimensional latent space that captures each agent's choice-profile and distances between agents in this latent space represent a metric of similarity between them. I propose exploring the high-dimensional choice-profile of agents to improve causal inference exercises in a series of ways.

Bernardo Modenesi is a Ph.D. candidate in Economics, also pursuing a masters degree in Statistics, at the University of Michigan. Bernardo's interests lie in interdisciplinary statistical methods, such as network theory and machine learning, for the improvement of causal inference exercises and economic modelling.