My research primarily focuses on semiparametric efficiency theory and nonparametric statistical methods in causal inference and survival analysis. Much of my research concerns obtaining valid statistical inference when using machine learning and other data-adaptive nuisance estimators. I am grateful to be funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development awards R01HD110462 and R01HD106108 for collaborative research with scientists in the UMass Amherst Department of Biostatistics and Epidemiology on causal pathways of cardiovascular disease following pregnancy complications and mitochondrial biomarkers of male reproductive health.

Below are some general themes of my ongoing and recent research and (overlapping) sets of papers in these areas.

Debiased inference for non-smooth functionals

Takatsu, K. and Westling, T. (2025). Debiased inference for a covariate-adjusted regression function. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 87(1):33-55. doi:10.1093/jrsssb/qkae041. arXiv:2210.06448

Bootstrap

Tang, Z. and Westling, T. (2024). Consistency of the bootstrap for asymptotically linear estimators with nuisance parameters. arXiv:2404.03064

Causal robustness and triangulation

Yang, J., Bhattacharhya, R., Lee, Y., and Westling, T. (2024). Statistical and Causal Robustness for Causal Null Hypothesis Tests. 40th Conference on Uncertainty and Artificial Intelligence. https://openreview.net/forum?id=6ZIQzAuMWE

Bhattacharya, R., Ocelli, I., and Westling, T. (2026). Robust Weighted Triangulation of Causal Effects Under Model Uncertainty. arXiv:2603.01119

Causal inference with continuous treatments

Westling, T. (2022) Nonparametric tests of the causal null with non-discrete exposures. Journal of the American Statistical Association, 117(539):1551-1562. doi:10.1080/01621459.2020.1865168. arXiv:2001.05344

Westling, T., Gilbert, P., and Carone, M. (2020). Causal isotonic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(3):719-747. doi:10.1111/rssb.12372. arXiv:1810.03269

Takatsu, K. and Westling, T. (2025). Debiased inference for a covariate-adjusted regression function. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 87(1):33-55. doi:10.1093/jrsssb/qkae041. arXiv:2210.06448

Causal inference with right-censored data

Westling, T., Luedtke, A., Gilbert, P., and Carone, M. (2024). Inference for treatment-specific survival curves using machine learning. Journal of the American Statistical Association, 119(546):1541-1553. doi:10.1080/01621459.2023.2205060. arXiv:2106.06602

Hu, R. and Westling, T. (2025). Nonparametric Sensitivity Analysis for Unobserved Confounding with Survival Outcomes. arXiv:2511.01412

Hu, R., Staudenmayer, J., Matthews, C., and Westling, T. (2024). Sensitivity of the Effect of Physical Activity on Mortality among Former Smokers to Unobserved Confounding and Confounder Misclassification.

Shape-constrained inference

Westling, T. and Carone, M. (2020). A unified study of nonparametric inference for monotone functions. Annals of Statistics, 48(2):1001-1024. doi:10.1214/19-AOS1835. arXiv:1806.01928.

Westling, T., Gilbert, P., and Carone, M. (2020). Causal isotonic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(3):719-747. doi:10.1111/rssb.12372. arXiv:1810.03269

Westling, T., van der Laan, M. J., and Carone, M. (2020). Correcting an estimator of a multivariate monotone function with isotonic regression. Electronic Journal of Statistics, 14(2):3032–3069. doi:10.1214/20-EJS1740. arXiv:1810.09022.

Westling, T., Downes, K. J., and Small, D. (2023). Nonparametric maximum likelihood estimation under a likelihood ratio order. Statistica Sinica, 33, 1-19. doi:10.5705/ss.202020.0207. arXiv:1904.12321

Wu, Y. and Westling, T. (2023). Nonparametric inference under a monotone hazard ratio order. Electronic Journal of Statistics, 17(2):3181-3225. doi:10.1214/23-EJS2173.

Ham, D., Westling, T., and Doss, C. (2024). Doubly robust estimation and inference for a log-concave counterfactual density. arXiv:2403.19917

Use of machine learning in biomedicine, epidemiology, and public health

Balzer, L. and Westling, T. (2021) Demystifying Statistical Inference When Using Machine Learning in Causal Research. American Journal of Epidemiology, kwab200. doi:10.1093/aje/kwab200.

Ramgopal S., Westling T., Siripong N., Salcido D., Martin-Gill C. (2021) Use of a metalearner to predict emergency medical services demand in an urban setting. Computational Methods and Programs in Biomedicine, 207:106201. doi:10.1016/j.cmpb.2021.106201.

Sawant S., Paskavitz, A., Rosati, A., Westling, T., Bertolla, R., Whitcomb, B., Pilsner, R. (2025+). A Machine Learning Approach Using Semen Parameters and Sperm Mitochondrial DNA Copy Number to Predict Couples’ Fecundity. F & S Reports, forthcoming. doi:10.1016/j.xfre.2025.05.002.

Analysis of observational studies

Harbison, R. A., Gray, A. J., Westling, T., Carone, M., Rodriguez, C. P., Futran, N., Cannon, R., and Houlton, J. J. (2020). The role of elective neck dissection in high-grade parotid malignancy: a hospital-based cohort study. The Laryngoscope, 130:1487-1495. doi:10.1002/lary.28238.

Westling, T., Cowden, C., Mwananyanda, L., et al. (2020). Impact of Chlorhexidine Baths on Suspected Sepsis and Bloodstream Infections in Hospitalized Neonates in Zambia. International Journal of Infectious Diseases, 96:54-60. doi:10.1016/j.ijid.2020.03.043.

Fisher, B. T., Westling, T., Boge, C. K., et al. (2021) Prospective Evaluation of Galactomannan and (1→3) Beta-D-Glucan Assays as Diagnostic Tools for Invasive Fungal Disease in Children, Adolescents and Young Adults with Acute Myeloid Leukemia Receiving Fungal Prophylaxis. Journal of the Pediatric Infectious Diseases Society, 10(8):864-871. doi:10.1093/jpids/piab036.

Yousem, D. M., Camargo, A., Yousem, K. P., Westling, T., and Carone, M. (2019). Ethical Dilemmas in Radiology: Survey of Opinions and Experiences. American Journal of Roentgenology, 213(6): 1274-1283. doi:10.2214/AJR.19.21121.

Analysis of vaccine trials

Saranya, S., Luedtke, A., Langevin, E., Zhu, M., Bonaparte, M., Machabert, T., Savarino, S., Zambrano, B., Moureau, A., Khromava, A., Moodie, Z., Westling T., et al. (2018). Effect of Dengue Serostatus on Dengue Vaccine Safety and Efficacy. New England Journal of Medicine, 379(4):327-340. doi:10.1056/NEJMoa1800820.

Westling, T., Juraska, M., Seaton, K., Tomaras, G., Gilbert, P., and Janes, H. (2020). Methods for comparing durability of immune responses between vaccine regimens in early-phase trials. Statistical Methods in Medical Research, 29(1):78-93. doi:10.1177/0962280218820881.