Kaitlyn Lee is a PhD candidate in Biostatistics at the University of California, Berkeley, mentored by Dr. Alejandro Schuler. She previously earned her MA in Biostatistics at UC Berkeley and her BA in Physics from Harvard University. Her research focuses on developing causal inference methods that combine machine learning and semiparametric statistics to produce statistically rigorous solutions for problems in health and social policy. Her current work centers on developing new machine learning algorithms that are flexible and computationally efficient for estimating causal effects. She has also interned at Genentech, where she worked on methods for incorporating covariates into the analysis of clinical trials.
Causal inference, machine learning, semiparametric efficiency, real world evidence, covariate adjustment