Physics and Astronomy Building Room 1434A
Many important causal questions concern interactions between units, also known as interference. Examples include interactions between individuals in households, students in schools, and firms in markets. Standard analyses that ignore interference can often break down in this setting: estimators can be badly biased, while classical randomization tests can be invalid. In this talk, I present recent results on testing for two-stage experiments, which are powerful designs for assessing interference. In these designs, whole clusters (e.g., households, schools, or graph partitions) are assigned to treatment or control; then units within each treated cluster are randomly assigned to treatment or control. I demonstrate how to construct powerful tests for non-sharp null hypotheses and use these results to analyze a two-stage randomized trial evaluating an intervention to reduce student absenteeism in the School District of Philadelphia. I discuss some extensions to more general forms of interference, as well as some current challenges. Paper here: https://arxiv.org/abs/1709.08036
I am currently a postdoctoral fellow in the Statistics Department at UC Berkeley where I am advised by Peng Ding. My research focuses on Causal Inference and Design of Experiments in the presence of interference. I got my PhD in Statistics at Harvard in 2018, under the supervision of Edo Airoldi. Before coming to Harvard I attended the Ecole Centrale Paris, where I studied Applied Mathematics and Engineering. I have lived in France, Israel, the US and Senegal, where I was born. I will start as an assistant professor in the MS&E and Statistics departments at Stanford in July 2019.