Research Bio
Sam Pimentel is an assistant professor in the Department of Statistics. His research centers on methodology for causal inference in observational studies. He develops new ways to form matched comparison groups in large observational datasets using approaches from discrete optimization. These tools allow transparent and interpretable inferences about the effects of interventions, and provide opportunities to study the impact of potential unobserved confounding variables. He is also interested in applying these methods in health services research, public policy, and the social sciences.
Research Expertise and Interest
causal inference, health services & policy analysis, biostatistics, discrete optimization
Teaching
Linear Models [STAT 230A]
Directed Study for Graduate Students [STAT 298]
Directed Study for Graduate Students [STAT 298]
Directed Study for Graduate Students [STAT 298]
Individual Study Leading to Higher Degrees [STAT 299]
Individual Study for Doctoral Candidates [STAT 602]
Linear Modelling: Theory and Applications [STAT 151A]
Directed Study for Graduate Students [STAT 298]
Directed Study for Graduate Students [STAT 298]
Directed Study for Graduate Students [STAT 298]
Individual Study Leading to Higher Degrees [STAT 299]
Individual Study for Doctoral Candidates [STAT 602]
Introductory Probability and Statistics for Business [STAT 21]
Individual Study Leading to Higher Degrees [STAT 299]
Individual Study Leading to Higher Degrees [STAT 299]