Dr. Caleb Miles works on developing semiparametric methods for causal inference and applying them to problems in public health. His applied work has largely been in HIV/AIDS, and he has more recently begun to work on psychiatric applications as well. Dr. Miles' current methodological research interests include the intersection between machine learning and causal inference, interference, mediation analysis, and measurement error.
Areas of Expertise
Data Science, Longitudinal Studies, Missing Data, Predictive Modeling/Machine Learning, Research Design and Methods, HIV/AIDS
Miles, C.H., Shpitser, I., Kanki, P., Meloni, S., & Tchetgen Tchetgen, E.J. "On semiparametric estimation of a path-specific effect in the presence of mediator-outcome confounding." Biometrika: In press, 2019.
Miles, C.H., Petersen, M., & van der Laan, M.J. "Causal Inference When Counterfactuals Depend on the Proportion of All Subjects Exposed." Biometrics: In press, 2019.
Miles, C.H., Schwartz, J., & Tchetgen Tchetgen, E.J. "A class of semiparametric tests of treatment effect robust to confounder measurement error." Statistics in Medicine: 37(24). 3403-3416, 2018.
Miles, C.H., Shpitser, I., Kanki, P., Meloni, S., & Tchetgen Tchetgen, E.J. "Quantifying an adherence path-specific effect of antiretroviral therapy in the Nigeria PEPFAR program." Journal of the American Statistical Association: 112(520). 1443-1452, 2017.
Miles, C.H., Kanki, P., Meloni, S., & Tchetgen Tchetgen, E.J. "On Partial Identification of the Natural Indirect Effect." Journal of Causal Inference: 5(2). 2017.