The Health Policy Data Science Lab is an informal group of interdisciplinary researchers focused on developing and using quantitative methods to solve problems in health policy with big data. Unique in its approach to data science, the Lab comprises scientists with varied statistical viewpoints, including frequentist and Bayesian, as well as both nonparametric and parametric modeling choices.
Similarly, our scientific areas of interest are broad, spanning trade-offs for multiple health outcomes, causal inference, methods for multiple data sources, computational health economics, comparative effectiveness research, and payment reform impact evaluation.
What unites the Lab is an emphasis on a priori-specified transparent methodological choices, sound statistical decision making, data-motivated methodology development in the area of health policy, and effective presentation of results. Check out our interview on Simply Statistics or videos of recent talks and interviews. See also "Big Data and the Future" and "Statisticians' Place in Big Data."
Graduate Students: Current Harvard students are welcome to contact Lab Faculty if they are interested in working with us. We are always happy to talk about possible projects with students.
From September through May, the Lab holds working lunch meetings at 12:30pm in the Department of Health Care Policy at Harvard Medical School, 180-A Longwood Ave.
September 7: Kate Lofgren
October 5: Katherine Donato
November 9: Nicole Maestas
December 14: Caroline Kelley
February 15: Christoph Kurz
March 22: Anas El Turabi
April 19: Sam Adhikari
May 24: TBA