Wen Wei Loh

Postdoctoral Research Fellow

Ghent University

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, health, and social sciences. My current research is in developing statistical methodologies for mediation analysis that can sharpen researchers' assessment of causal mechanisms in experimental and observational study designs.

Wen Wei LOH

Research

Google scholar profile

GitHub

Loh, W. W., and Ren, D. (2021). Data-driven covariate selection for confounding adjustment by focusing on the stability of the effect estimator.
Submitted.
Preprint | R code on GitHub

Loh, W. W., and Ren, D. (2021). Improving causal inference of mediation analysis with multiple mediators using interventional indirect effects.
Submitted.
Preprint

Rosseel, Y., and Loh, W. W. (2021). The “Structural-After-Measurement” (SAM) approach to SEM.
Revise and resubmit.
Preprint

Bogaert, J., Loh, W. W., and Rosseel, Y. (2021). A small sample correction for factor score regression.
Submitted.

Loh, W. W., and Rosseel, Y. (2021). Investigating the impact of omitted item-specific effects on causal effect estimation.
In preparation.

Loh, W. W., and Kim J.-S. (2021). Evaluating sensitivity to classification uncertainty in subgroup effect analyses.
In preparation.
Preprint

Loh, W. W., and Vansteelandt, S. (2021). Sensitivity analysis for unmeasured confounding using effect extrapolation.
In preparation.
Preprint

Loh, W. W., and Ren, D. (2021). Estimating social influence in a social network using potential outcomes.
Psychological Methods. Advance online publication.
Preprint | Paper | R code on GitHub

Ren, D., Stavrova, O., and Loh, W. W. (2021). Nonlinear effect of social interaction quantity on psychological well-being: Diminishing returns or inverted U?
Journal of Personality and Social Psychology. Advance online publication.
Paper

Cai, X., Loh, W. W., and Crawford, F. W. (2021). Identification of causal intervention effects under contagion.
Journal of Causal Inference, 9(1), 9-38.
Paper

Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2021). Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators.
Psychological Methods, Advance online publication.
Preprint | Paper | Online supplemental materials | 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). Heterogeneous indirect effects for multiple mediators using interventional effect models.
Epidemiologic Methods, 9(1).
Preprint | Paper | R code on GitHub

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. 40, 607-630.
Paper | R code on GitHub

Loh, W. W., Hudgens M.G., Clemens J.D., Ali M., and Emch, M.E. (2020). Randomization inference with general interference and censoring.
Biometrics, 76(1), 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

Lipid-TargetVsNuis-3d-CO.png

Contact

Contact me: wenwei.loh[at]ugent.be.
Official address: Department of Data Analysis, Henri Dunantlaan 1, 9000 Gent, Belgium

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.