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Wen Wei LohAssistant Professor | QTM


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
  • B.Sc., Mathematics and Statistical Science, University College London, 2005


Dr. Loh enjoys working on causal inference for applications motivated by behavioral and psychological sciences toward improving health outcomes. His current research is in developing and promoting statistical methodologies for longitudinal data analysis that can sharpen researchers' assessment of causal mechanisms in observational study designs.

Selected Publications

Loh, W. W., and Ren, D. (2023). G-formula: what it is, why it matters, and how to implement it in lavaan.Loh, W. W., and Ren, D. (In press). A tutorial on causal inference in longitudinal data with time-varying confounding using g-estimationAdvances in Methods and Practices in Psychological Science.Loh, W. W., and Ren, D. (In press). Understated gender disparities due to outcome-dependent selection: Comment on Mackelprang et al. (2022). American Psychologist.Loh, W. W., and Kim J.-S. (2023). Causal models. International Encyclopedia of Education (Fourth Edition), Pages 670-683.Kim, J.-S., Liao, X., and Loh, W.W. (2023). Comparing Parametric and Nonparametric Methods for Heterogeneous Treatment Effects. Quantitative Psychology
Psychological Methods. 
Loh, W. W., and Ren, D. (2022). Improving causal inference of mediation analysis with multiple mediators using interventional indirect effectsSocial and Personality Psychology Compass, e12708.       
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, 27(6), 982-999.                                    
R code on GitHub           
Rosseel, Y., and Loh, W. W. (In press). A structural after measurement approach to structural equation modelingPsychological Methods. 
Loh, W. W., and Kim J.-S. (2022). Evaluating sensitivity to classification uncertainty in subgroup effect analysesBMC Medical Research Methodology. 
R code on GitHub                       
Bogaert, J., Loh, W. W., and Rosseel, Y. (2022). A small sample correction for factor score regression.
Educational and Psychological Measurement.   
Psychological Methods, 27(5), 841-855.       
R code on GitHub               
Ren, D., Stavrova, O., and Loh, W. W. (2022). Nonlinear effect of social interaction quantity on psychological well-being: Diminishing returns or inverted U?  Journal of Personality and Social Psychology, 122(6), 1056–1074.
Cai, X., Loh, W. W., and Crawford, F. W. (2021). Identification of causal intervention effects under contagionJournal of Causal Inference, 9(1), 9-38 
Loh, W. W., Moerkerke B., Loeys T., and Vansteelandt S. (2020). Heterogeneous indirect effects for multiple mediators using interventional effect modelsEpidemiologic Methods, Accepted.
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 unknownBiometrics, Accepted.
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.

Loh, W. W., and Vansteelandt S. (2020). Confounder selection strategies targeting stable treatment effect estimatorsStatistics In Medicine, Accepted.
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 censoringBiometrics, 235-245.
R code on GitHub

Loh, W. W., Richardson, T. S., and Robins, J. M. (2017). An apparent paradox explainedStatistical Science, 32(3), 356-361.

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.
R package

Loh, W. W., and Richardson, T. S. (2015). A finite population likelihood ratio test of the sharp null hypothesis for compliersIn Thirty-First Conference on Uncertainty in Artificial Intelligence.
R package


  • QTM 210: Probability and Statistics
  • QTM 530: Computing I 


Introduction to Causal Inference using Causal Diagrams and Potential Outcomes

SPSP 2023 Professional Development Workshop, Atlanta, GA