Professor of Epidemiology
677 Huntington Avenue, Kresge 825
Boston, Massachusetts 02115
- Member, Harvard-MIT Division of Health Sciences & Technology Faculty
- Associate Director, HSPH Program on Causal Inference
- Member, Biostatistics and Computational Biology Program, Dana Farber/Harvard Cancer Center
- Chair-Elect, Section on Statistics in Epidemiology, American Statistical Association
M.D., 1995, Universidad Autónoma de Madrid, Spain
M.P.H., 1996, Harvard University
Sc.M. (Biostatistics), 1999, Harvard University
Dr.P.H. (Epidemiology), 1999, Harvard University
My research and teaching are focused on methodology for causal inference, including comparative effectiveness research to guide policy and clinical decisions.
In an ideal world, most decisions would be based on randomized experiments. For example, public health recommendations to avoid saturated fat or medical prescription of a particular painkiller would be supported by long-term studies that compared the effects of interventions randomly assigned to large groups of people from the target population who complied with their assignment. Unfortunately, randomized experiments are often unethical, impractical, or simply too lengthy for timely decisions.
The next best thing to a randomized experiment is an observational study that closely mimics a randomized experiment. Though causal inferences from observational data are risky, the best available evidence for decision-making will often come from well designed and properly analyzed observational studies. Because there is no alternative to observational studies, we need to keep improving them.