Loh, W. W., and Ren, D. (In press). Adjusting for baseline measurements of the mediators and outcome as a first step toward eliminating confounding biases in mediation analysis.
Perspectives on Psychological Science.
Preprint

Loh, W. W., and Ren, D. (2022). Improving causal inference of mediation analysis with multiple mediators using interventional indirect effects.
Social and Personality Psychology Compass, e12708.
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

Rosseel, Y., and Loh, W. W. (In press). A structural after measurement approach to structural equation modeling.
Psychological Methods.
Preprint | Paper

Ananth, C., and Loh, W. W. (2022). Understanding etiologic pathways through multiple sequential mediators: an application in perinatal epidemiology.
Epidemiology.
Paper | R code on GitHub

Loh, W. W., and Kim J.-S. (2022). Evaluating sensitivity to classification uncertainty in subgroup effect analyses.
BMC Medical Research Methodology.
Paper | Preprint | R code on GitHub

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

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

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 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. (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