Foundations of Data Science
Computational Social Science
I’m an Assistant Professor of Biostatistics in the Mailman School of Public Health at Columbia University. My research focuses mostly on causal inference: developing statistical methods and machine learning tools to support inference about treatment effects, interventions, and policies. I often take a perspective informed by graphical models (e.g., DAGs/Bayesian networks or related). Current research topics include structure learning (a.k.a. causal discovery), semiparametric inference, time series analysis, and missing data. I also work on algorithmic fairness: understanding and counteracting the biases introduced by data science tools deployed in socially-impactful settings. Finally, I have interests in the philosophy of science and the foundations of statistics.