Jacqueline Mauro is a Ph.D. student at Carnegie Mellon University.
I am a doctoral student in statistics, joint with public policy, at Carnegie Mellon University under Edward Kennedy. I work on social science problems from International Trade to Criminology, developing new nonparametric causal inference methods that can be used by practitioners across a variety of fields. Our estimators lean on developments in Machine Learning to create flexible yet robust estimates of causal effects.
I aim to develop new methods of analysis that can tell us about the effects of policy-interventions that matter. For example, how does the distance from home affect prisoners' chances of staying out of jail? How does an open trade policy affect workers in small exporting countries?
I am also developing new sensitivity analyses for these estimators in the IV setting. In this case, although we don't need to make strong parametric assumptions, we still must make strong assumptions about the causal structure of the data.
Before graduate school, I worked as a Research Assistant at RAND. There, my team provided recommendations to the Air Force about the effects and sources of stress for the ICBM force. I also worked on a team which developed a nationwide survey for victims of crime.
I graduated from Barnard College with a BA in Economics in 2010, and earned my MA from Columbia University in Quantitative Methods in Social Sciences in 2011.
My research is focused on developing tools for inference that rely on few assumptions and inherit the flexibility and computational strength of machine learning. I work with modern semi-parametric instrumental variables methods, working to extend these to policy settings. I am focused now on developing estimators and bounds for continuous instruments, continuous treatments and longitudinal settings.