Department:
Biostatistics
Bio/CV:
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 primary research interest lies in developing causal inference methods that leverage machine learning and semiparametric statistics to create statistically rigorous solutions for critical problems in health and social policy. Her current work has focused on developing new machine learning algorithms that offer flexible and computationally efficient methods to pinpoint causal effects.
Research interests:
Causal inference, machine learning, semiparametric efficiency, real world evidence