About me

I am an Assistant Professor in the Statistics Department at UC Berkeley. My research aims at understanding causal relationships using large administrative datasets from the medical and social sciences. I develop new ways to form compelling matched or weighted comparison groups in these datasets using tools from optimization. I also study methods for transparent and interpretable inference about causal effects when unobserved confounding variables may be present. I am broadly interested in substantive problems relating to people and institutions, and my methodological work is motivated by collaborations to address such problems in health services research, epidemiology, and education.

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Research

PREPRINTS

Pimentel, S.D., and Yu, R. (2024+). Re-evaluating the impact of hormone replacement therapy on heart disease using match-adaptive randomization inference. arxiv:2403.01330.

Huang, M., Soriano, D., and Pimentel, S.D. (2024+). Design sensitivity and its implications for weighted observational studies. arxiv:2307.00093.

Huang, M., and Pimentel, S.D. (2024+). Variance-based sensitivity analysis for weighting estimators results in more informative bounds. arxiv:2208.01691. Won 2023 Best Theory Poster Award from the Society for Political Methodology.

Pimentel, S.D. and Huang, Y. (2024+). Covariate-adaptive randomization inference for matched designs. arxiv:2207.05019.

Liao, L.D., and Pimentel, S.D. (2024+). JOINTVIP: Prioritizing variables in observational study design with joint variable importance plot in R. arxiv:2302.10367.

PUBLICATIONS

Statistical Methodology

Liao, L.D., Zhu, Y., Ngo, A.L., Chehab, R.F., and Pimentel, S.D. (2024+). Prioritizing variables for observational study design using the joint variable importance plot. The American Statistician (in press). arxiv:2301.09754.

Soriano, D., Ben-Michael, E., Bickel, P.J., Feller, A., and Pimentel, S.D. (2023). Interpretable sensitivity analysis for balancing weights. Journal of the Royal Statistical Society - Series A 186(4), 707-721. arxiv:2102.13218.

Glazer, A.K., and Pimentel, S.D. (2023). Robust inference for matching under rolling enrollment. Journal of Causal Inference 11(1), 2022-0055. arxiv:2205.01061.

Howard, S.R., and Pimentel, S.D. (2021). The uniform general signed rank test and its design sensitivity. Biometrika 108, 381-396. arXiv:1904.08895.

Pimentel, S.D., and Kelz, R.R. (2020). Optimal tradeoffs in matched designs comparing US-trained and internationally trained surgeons. Journal of the American Statistical Association 115 (532), 1675-1688. Download

Pimentel, S.D., Forrow, L.V., Gellar, J., and Li, J. (2020). Optimal matching approaches in health policy evaluations under rolling enrolment. Journal of the Royal Statistical Society - Series A 183 (4), 1411-1435. Download

Keele, L., Harris, S., Pimentel, S.D., and Grieve, R. (2020). Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to the ICU in the UK. Journal of the Royal Statistical Society - Series A 183 (4), 1501-1521. Download

Keele, L., and Pimentel, S.D. (2019). Matching with attention to effect modification in a data challenge. Observational Studies 5, 83-92.

Pimentel, S.D., Page, L., Lenard, M., and Keele, L. (2018). Optimal multilevel matching using network flows: an application to a summer reading intervention. Annals of Applied Statistics, 12:3, 1479-1505. Download

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. Download

Statistical Applications

Kuzniewicz, M.W., Campbell, C.I., Li, S., Walsh, E.M., Croen, L.A., Comer, S.D., Pimentel, S.D., Hedderson, M. and Sun, L.S. (2022). Accuracy of diagnostic codes for prenatal opioid exposure and neonatal opioid withdrawal syndrome . Journal of Perinatology, 1-7.

Silber, J.H., Rosenbaum, P.R., Pimentel, S.D., Calhoun, S., Wang, W., Sharpe, J.E., Reiter, J.G., Shah, S.A., Hochman, L.A., and Even-Shoshan, O. (2019). Comparing resource use in medical admissions of children with complex chronic conditions. Medical Care, 57 (8), 615-624.

Zaheer, S., Pimentel, S.D., Simmons, K.D., Kuo, L.E.Y, Datta, J., Williams, N., Fraker, D.L., and Kelz, R.R. (2017). Comparing international and United States undergraduate medical education and surgical outcomes using a refined balance methodology. Annals of Surgery, 265 (5), 916-922.

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

Liao., L.D., and Pimentel, S.D. (2023). R package jointVIP: Prioritize Variables with Joint Variable Importance Plot in Observational Study Design. Published on The Comprehensive R Archive Network.

Han, S., and Pimentel, S.D. (2022). R package MultiObjMatch: Multi-Objective Matching Algorithm. Published on The Comprehensive R Archive Network.

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.

Invited Chapters

Pimentel, S.D. (2023). Fine balance and its variations in modern optimal matching. In Handbook of Matching and Weighting Adjustments for Causal Inference, eds. Zubizarreta, J.R., Stuart, E. A., Small, D.S., and Rosenbaum, P.R. CRC Press: Boca Raton, FL.

Keele, L., and Pimentel, S.D. (2023). Matching with multilevel data. In Handbook of Matching and Weighting Adjustments for Causal Inference, eds. Zubizarreta, J.R., Stuart, E. A., Small, D.S., and Rosenbaum, P.R. CRC Press: Boca Raton, FL.