Wen Wei LohAssistant Professor
Biography
Dr. Loh is currently completing year two of a two-year post-doctoral fellowship at Ghent University in Belgium. Prior to joining Ghent University, he worked as a postdoctoral research fellow in Biostatistics at the University of North Carolina Chapel Hill. Dr. Loh received his PhD in Statistics from University of Washington and MA in Statistics from Harvard University.
Starting in Fall 2021, Dr. Loh will be onboarded as an Assistant Professor in the Department of Quantitative Theory & Methods at Emory University.
Education
- Ph.D., Statistics, University of Washington, 2016
- M.A., Statistics, Harvard University, 2006
- BSc., Mathematics and Statistical Science, University College, 2005
Research
Publications
Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2020). Heterogeneous indirect effects for multiple mediators using interventional effect models.
Epidemiologic Methods, Accepted.
Preprint
R code on GitHub
Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2020). Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown.
Biometrics, Accepted.
Paper
R code on GitHub
Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2020). Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators.
Psychological Methods, Accepted.
Preprint
Loh, W. W., Moerkerke B., Loeys T., Poppe L., Crombez G., and Vansteelandt S. (2020). Estimation of controlled direct effects in longitudinal mediation analyses with latent variables in randomised studies.
Multivariate Behavioral Research. 55(5), 763-785
Paper
Loh, W. W., and Vansteelandt S. (2020). Confounder selection strategies targeting stable treatment effect estimators.
Statistics In Medicine, Accepted.
Paper
R code on GitHub
Loh, W., & Ren, D. (2020). Estimating social influence in a social network using potential outcomes.
Psychological Methods, Accepted.
Paper
Preprint
Loh, W. W., and Kim J. (2020). Evaluating the impact of misclassification when estimating heterogeneous causal effects.
Under review
Loh, W. W., Hudgens M.G., Clemens J.D., Ali M., and Emch, M.E. (2020). Randomization inference with general interference and censoring.
Biometrics, 235-245.
Paper
R code on GitHub
Loh, W. W., Richardson, T. S., and Robins, J. M. (2017). An apparent paradox explained.
Statistical Science, 32(3), 356-361.
Paper
Rigdon, J., Loh, W. W., and Hudgens, M. G. (2017). Response to comment on 'Randomization inference for treatment effects on a binary outcome'.
Statistics in Medicine, 36(5), 876-880.
Paper
R package
Loh, W. W., and Richardson, T. S. (2015). A finite population likelihood ratio test of the sharp null hypothesis for compliers.
In Thirty-First Conference on Uncertainty in Artificial Intelligence.
Paper
R package
Loh, W. W., and Richardson, T. S. (2013). A finite population test of the sharp null hypothesis for compliers.
In UAI Workshop on Approaches to Causal Structure Learning, 15 July, Bellevue, Washington.
Paper