I received my PhD in Statistics from University of Washington and MA in Statistics from Harvard University.
Prior to joining Ghent University, I worked as a postdoctoral research fellow in Biostatistics at the University of North Carolina Chapel Hill.

I enjoy working on causal inference for applications motivated by behavioral and social sciences. My current research focuses on developing statistical methods for inferring causal effects in mediation analyses.

I enjoy working on causal inference for applications motivated by behavioral and social sciences. My current research focuses on developing statistical methods for inferring causal effects in mediation analyses.

Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2019).
Disentangling indirect effects through multiple mediators whose causal structure is unknown.

Under review

Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2019).
Interventional effect models for multiple mediators.

Paper on arXiv

R code on GitHub

Loh, W. W., Moerkerke B., Loeys T., Poppe L., Crombez G., and Vansteelandt S. (2019).
Estimation of controlled direct effects in longitudinal mediation analyses with latent variables in randomised studies.
*Multivariate Behavioral Research. Accepted for publication.
*

*Loh, W. W., Hudgens M.G., Clemens J.D., Ali M., and Emch, M.E. (2019).
Randomization inference with general interference and censoring.
Biometrics. Accepted Author Manuscript.
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

We think in terms of what would happen (**potential outcomes**),
and what could have happened (**counterfactuals**).
The idea of simulating potential outcomes is nicely described in this episode of the TV show **Person of Interest**:
If-Then-Else.