James Robins

James Robins

James Robins is a prominent American epidemiologist and biostatistician known for his significant contributions to the field of causal inference in epidemiology and statistics. He is a professor at the Harvard T.H. Chan School of Public Health.

Early Life and Education

James Robins was born in New York City. He completed his undergraduate studies at Harvard College, where he majored in mathematics. He then went on to earn his M.D. from Washington University in St. Louis.

Career

Robins began his career as a practicing physician before transitioning to the field of epidemiology. He joined the faculty at the Harvard T.H. Chan School of Public Health where he has been a professor for several decades. His work primarily focuses on developing methods for drawing causal inferences from observational data.

Contributions to Epidemiology

James Robins is best known for his development of Marginal Structural Models (MSMs) and the G-computation algorithm. These methods have been widely adopted in the field of epidemiology for estimating causal effects in the presence of time-varying confounding.

Marginal Structural Models

Marginal Structural Models are a class of statistical models used to estimate causal effects in longitudinal studies. They are particularly useful in situations where there are time-dependent confounders that are affected by prior treatment.

G-computation Algorithm

The G-computation algorithm is a method for estimating the causal effect of a treatment or intervention. It is based on the G-formula, which provides a way to adjust for confounding variables in observational studies.

Awards and Honors

James Robins has received numerous awards for his contributions to the field of epidemiology and biostatistics. These include the R.A. Fisher Award and the Nathan Mantel Award.

Selected Publications

• Robins, J.M. (1986). "A new approach to causal inference in mortality studies with a sustained exposure period—Application to control of the healthy worker survivor effect." Mathematical Modelling.
• Robins, J.M., Hernán, M.A., Brumback, B. (2000). "Marginal structural models and causal inference in epidemiology." Epidemiology.

See Also

• Epidemiology
• Biostatistics
• Causal inference
• Harvard T.H. Chan School of Public Health