Wen Wei LohAssistant Professor

Dr. Wen Wei Loh joined the Emory QTM team in Fall 2022. He comes to us via a 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.  

• Ph.D., Statistics, University of Washington, 2016
• M.A., Statistics, Harvard University, 2006
• BSc., Mathematics and Statistical Science, University College, 2005

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

• QTM 210: Probability and Statistics