The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

Causal inference, at the intersection of statistics and machine learning, is an active field of research that develops methods and algorithms for the data-driven derivation and analysis of ...

Data really powers everything that we do. Research activities in the data science area are concerned with the development of machine learning and computational statistical methods, their theoretical ...

Faculty in the Statistical Learning and Data Science Hub advance statistical and machine learning methods tailored to the unique challenges of biomedical and epidemiologic data, including ...

The use of machine learning in statistics production is being explored widely, with applications including coding, outlier detection, and imputing missing values. Relatively little work has so far ...