About me

I am an Assistant Professor in the Statistics Department at UC Berkeley. My research centers on methodology for causal inference in observational studies. I develop new matching designs that leverage discrete optimization algorithms to balance multiple goals and that exhibit reduced sensitivity to possible unmeasured biases. I am also interested in applications of these methods in public policy, public health, medicine, and the social sciences.

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Research

PUBLICATIONS

Statistical Methodology

Pimentel, S.D., Small, D.S. , and Rosenbaum, P.R. (2017). An exact test of fit for the Gaussian linear model using optimal nonbipartite matching. Technometrics, 59 (3), 330-337. Download

Pimentel, S.D., Small, D.S., and Rosenbaum, P.R. (2016). Constructed second control groups and attenuation of unmeasured biases. Journal of the American Statistical Association, 111 (515), 1157-1167. Download

Pimentel, S.D., Kelz, R.R., Silber, J.H., and Rosenbaum, P.R. (2015). Large, sparse optimal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons. Journal of the American Statistical Association 110 (510), 515-527. Download

Pimentel, S.D., Yoon, F., and Keele, L. (2015). Variable-ratio matching with fine balance in a study of the Peer Health Exchange. Statistics in Medicine, 34 (30) 4070-4082. doi:10.1002/sim.6593. Download

Statistical Applications

Zaheer, S., Pimentel, S.D., Simmons, K.D., Kuo, L.E.Y, Datta, J., Williams, N., Fraker, D.L., and Kelz, R.R. (2016). Comparing international and United States undergraduate medical education and surgical outcomes using a refined balance methodology. Annals of Surgery (to appear).

Grossman, G., Gazal-Ayal, O., Pimentel, S.D., and Weinstein, J. (2016). Descriptive representation and judicial outcomes in multi-ethnic societies. American Journal of Political Science, 60 (1), 44-69. doi:10.1111/ajps.12187.

Software for Statistics and Data Visualization

Pimentel, S.D. (2016). Large, sparse optimal matching with R package rcbalance. Observational Studies, 2, 4-23.

Pimentel, S.D., and Keele, L. (2016). R package matchMulti: Optimal Multilevel Matching using a Network Algorithm. Published on The Comprehensive R Archive Network.

Pimentel, S.D. (2016). R package rcbsubset: Optimal Subset Matching with Refined Covariate Balance. Published on The Comprehensive R Archive Network.

Pimentel, S.D. (2014). R package rcbalance: Large, Sparse Optimal Matching with Refined Covariate Balance. Published on The Comprehensive R Archive Network.

Pimentel, S.D. (2014). Choosing a clustering: an a posteriori method for social networks. Journal of Social Structure Vol. 15, No. 1.

Pimentel, S., Walbot, V., and Fernandes, J. (2011). GRFT – genetic records family tree web applet. Frontiers in Genetics 2.

IN PREPARATION

For copies of these manuscripts, please contact the author.

Pimentel, S.D., and Kelz, R.R. (2016). “Optimal tradeoffs in matching designs for observational studies.”

Pimentel, S.D., Page, L., Lenard, M., and Keele, L. (2016). "Optimal multilevel matching using network flows: an application to a summer reading intervention."

Keele, L., Harris, S., Pimentel, S.D., and Grieve, R. (2016). "Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to the ICU in the UK."