I am a Moore-Sloan faculty fellow at New York University’s Center for Data Science. I research methods for empirical political science in settings in which data is scarce and causal inference is hard. I address problems in this setting by combining machine learning and causal inference methodology. My applied research focuses on understanding the causes and consequences of mass public action, and on corruption and bribery in developing countries.
I received my PhD in political methodology at Duke University, where I worked with David Siegel, Cynthia Rudin’s Prediction Analysis Lab, the inter-departmental Almost Matching Exactly Lab, and Devlab@Duke.
I can be reached via email at: marco[dot]morucci[at]nyu[dot]edu.