I am an econometrician in the OIT group at Stanford GSB. My current research focusses broadly on two related themes:
High-dimensional and robust causal inference, including work on using machine learning to improve inferences from randomized trials, robust inference in panel data, synthetic control, matching estimation, highly over-parametrized models, and high-dimensional outcome data;
Data-driven decision-making with misaligned objectives, including work on algorithmic fairness, human–AI interaction, the regulation of algorithms, and the design of pre-analysis plans.
I hold a PhD in economics from Harvard University. Previously, I obtained a master’s degree in public policy from the Harvard Kennedy School. My background is in mathematics with a focus on probability theory and combinatorics, which I studied at the University of Cambridge (Part III of the Mathematical Tripos) and the Technical University of Munich. I also studied and worked in Hangzhou, China and Ouagadougou, Burkina Faso.