Publications

Selected publications organized by topic are listed below. For a full list (including preprints), please see my Google Scholar profile.

Causal Mediation Analysis

Methodological Development

Loh, W. W. (In press). Analyzing multiple mediators in multiple single-mediator models leads to wrong conclusions. Proceedings of the National Academy of Sciences (PNAS). R code on OSF

Loh, W. W., & Ananth, C. V. (2025). Does adjusting for causal intermediate confounders resolve the perinatal crossover paradox? Epidemiology. Paper | R code on GitHub

Distinguishes the causal assumptions underlying exposure-dependent confounders and mediators, and their implications on nonparametric identification of interventional (in)direct effects.

Loh, W. W. (2024). Unmeasured mediator-outcome confounding distorts both indirect and direct effects. Proceedings of the National Academy of Sciences (PNAS). Paper | R code on GitHub

Loh, W. W., & Ren, D. (2023). Adjusting for baseline measurements of the mediators and outcome as a first step toward eliminating confounding biases in mediation analysis. Perspectives on Psychological Science. Paper

Examples of when adjusting for baseline measurements can strengthen or weaken the "no unmeasured confounding" assumption.

Loh, W. W., Moerkerke, B., Loeys, T., & Vansteelandt, S. (2022). Disentangling indirect effects through multiple mediators without assuming any causal structure among the mediators. Psychological Methods. Paper | Online Supplemental Materials | R code on GitHub

Introduces interventional indirect effects to psychology.

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

Challenges of using serial mediation models, and why interventional indirect effects are robust to such vulnerabilities.

Loh, W. W., Moerkerke, B., Loeys, T., & Vansteelandt, S. (2022). Nonlinear mediation analysis with high-dimensional mediators whose causal structure is unknown. Biometrics. Paper | R code on GitHub

Loh, W. W., Moerkerke, B., Loeys, T., & Vansteelandt, S. (2020). Heterogeneous indirect effects for multiple mediators using interventional effect models. Epidemiologic Methods. Paper | R code on GitHub

Loh, W. W., Moerkerke, B., Loeys, T., Poppe, L., Crombez, G., & Vansteelandt, S. (2020). Estimation of controlled direct effects in longitudinal mediation analyses with latent variables in randomized studies. Multivariate Behavioral Research. Paper

Introduces g-estimation for a longitudinal mediator in the presence of time-varying confounding.

Applications

Lasch, F., Guizzaro, L., & Loh, W. W. (In print). Comparison of g-estimation approaches for handling symptomatic medication at multiple timepoints in Alzheimer's Disease with a hypothetical strategy. Statistics in Biopharmaceutical Research. Preprint

Ananth, C. V., & Loh, W. W. (2024). Causal effects of competing obstetrical interventions: mediators of placental abruption and perinatal mortality. American Journal of Epidemiology. Paper | R code on GitHub

Editor’s Choice (best paper in the issue)

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

Seward, N., Peters, T. J., Loh, W. W., ..., & Araya R. (2025). Explaining how a psychosocial intervention (PROACTIVE) based on behavioural activation improved outcomes of depression in older adults living in deprived regions of Brazil: the mediating roles of reduced loneliness and stepped care. Journal of Affective Disorders.  Paper

Seward, N., Loh, W. W., ... & Araya, R. (2025). Understanding how digital mental health interventions can be optimised to improve longer term sustainability: findings from a causal mediation analysis of the CONEMO trials. PLOS Global Public Health. Paper

Murillo, C., Galán-Martín, M. Á., Montero-Cuadrado, F., Lluch, E., Meeus, M., & Loh, W. W. (2023). Reductions in kinesiophobia and distress after pain neuroscience education and exercise lead to favourable outcomes: a secondary mediation analysis of a randomized controlled trial in primary care. Pain. Paper

Ren, D., Wesselmann, E. D., Loh, W. W., van Beest, I., van Leeuwen, F., & Sleegers, W. W. (2023). Do cues of infectious disease shape people’s affective responses to social exclusion? Emotion. Paper

Causal Inference for Longitudinal Designs

Loh, W. W., & Jorgensen, T. D. (In press). A tutorial on estimating dynamic treatment regimes from observational longitudinal data using lavaan. Psychological Methods. Paper | Preprint and R code on OSF

Introduces dynamic treatment regimes (or adaptive interventions) and estimation with naturally occurring longitudinal data using an implementation in lavaan.

Loh, W. W., Ren, D., & West, S. G. (2024). Parametric g-formula for testing time-varying causal effects: What it is, why it matters, and how to implement it in lavaan. Multivariate Behavioral Research. Paper | R code on GitHub

First introduction of Robins's parametric g-formula to psychology and an implementation using lavaan.
Top 10 most downloaded articles in the last three years (over 3,555 times) as of October 2025.

Loh, W. W. (2024). Estimating curvilinear time-varying treatment effects: Combining g-estimation of structural nested mean models with time-varying effect models for longitudinal causal inference. Psychological Methods. Paper | Preprint | R code on GitHub

Loh, W. W., & Ren, D. (2023). A tutorial on causal inference in longitudinal data with time-varying confounding using g-estimation. Advances in Methods and Practices in Psychological Science. Paper

Introduces Robins's g-estimation using a simple example, and an implementation using lavaan.

Loh, W. W., & Ren, D. (2023). Estimating time-varying treatment effects in longitudinal studies. Psychological Methods. Paper | Preprint | R code on GitHub

First introduction of Robins's g-estimation of a structural nested mean model (SNMM) to psychology.

Loh, W. W., & Ren, D. (2023). The unfulfilled promise of longitudinal designs for causal inference. Collabra: Psychology. Paper

Explains, using causal diagrams, why even in the simplest possible scenario with just two time points, demonstrating causality can be complicated.

Causal Inference Methods for Public Health and Psychology

Loh, W. W., & Ren, D. (2025). Upscaling behavioral interventions requires addressing selection bias. Proceedings of the National Academy of Sciences (PNAS). Paper | R code on OSF

An example of how outcome-associated study selection can lead to selection bias (even in the absence of colliders), and an application of a sensitivity analysis using inverse probability of selection weights.

Loh, W. W., & Ren, D. (2025). From experiments to policy insights: Generalizing causal effects from study samples to target populations. Advances in Methods and Practices in Psychological Science. Paper | R code on OSF

A nontechnical tutorial introducing the causal effect generalizability framework to psychology.

Ren, D., Stavrova, O., van Beest, I., van Dijk, E., & Loh, W. W. (2025). Investigating lived ostracism: Valid causal inference requires articulating causal questions. Journal of Social Psychology. Paper | Preprint

An accessible, nontechnical introduction to causal inference for social psychologists of ostracism research.

Loh, W.W., Ren, D. & Rosseel, Y. (2025). Rethinking causal inference for recurring exposures: The incremental propensity score approach with lavaan. Behavior Research Methods. Paper | R code on GitHub

An implementation of Kennedy's IPS approach for recurring exposures using lavaan to concurrently estimate causal effects for repeated outcomes.

Loh, W. W., & Ren, D. (2024). The incremental propensity score approach for diversity science. Advances in Methods and Practices in Psychological Science. Paper | R code on GitHub

An accessible introduction to Kennedy's Incremental Propensity Score (IPS), an alternative approach to quantify causal effects, to psychology.

Loh, W. W. (2025). Causal inference with unobserved confounding: Leveraging negative control outcomes using lavaan. Multivariate Behavioral Research. Paper | R code on GitHub

An accessible introduction to Tchetgen Tchetgen's Control Outcome Calibration Approach (COCA), for using negative control outcomes to counteract unmeasured confounding bias, and an implementation in lavaan.

Loh, W. W. (2025). Doubly robust control outcome calibration approach estimation of conditional effects with uncontrolled confounding. Epidemiology. Paper

Loh, W. W., & Ren, D. (2024). Enhancing causal pursuits in organizational science: targeting the effect of treatment on the treated in research on vulnerable populations. Organizational Research Methods. Paper | R code on GitHub

Introduces the Effect of Treatment on the Treated (ETT), an alternative approach to quantify causal effects, to organizational research.

Ren, D., & Loh, W. W. (2024). Advancing group-based disparities research and beyond: A cautionary note on selection bias. Advances in Methods and Practices in Psychological Science. Paper | Supplemental Online Materials & R scripts on OSF

Highly accessible explanations of how common study designs for investigating group-based disparities are prone to selection bias (even in the absence of colliders).

Poppe, L., Steen, J., Loh, W. W., Crombez, G., De Block, F., Jacobs, N., … Paepe, A. L. D. (2024). How to develop causal directed acyclic graphs for observational health research: a scoping review. Health Psychology Review. Paper

Worked example from health psychology research of how to develop a causal diagram.

Loh, W. W., & Ren, D. (2023). Data-driven covariate selection for confounding adjustment by focusing on the stability of the effect estimator. Psychological Methods. Paper | Preprint | R code on GitHub

Loh, W. W., & Vansteelandt, S. (2021). Confounder selection strategies targeting stable treatment effect estimators. Statistics in Medicine. Paper | R code on GitHub

Loh, W. W., & Ren, D. (2022). Estimating social influence in a social network using potential outcomes. Psychological Methods. Paper

Introduces how treatment interference (a violation of Rubin's SUTVA) can arise in psychology and how to estimate peer influence or spillover effects given a network structure.

Cai, X., Loh, W. W., & Crawford, F. W. (2021). Identification of causal intervention effects under contagion. Journal of Causal Inference. Paper

Loh, W. W., Hudgens, M. G., Clemens, J. D., Ali, M., & Emch, M. E. (2020). Randomization inference with general interference and censoring. Biometrics. Paper | R code on GitHub

Rigdon, J., Loh, W. W., & Hudgens, M. G. (2017). Response to comment on ‘Randomization inference for treatment effects on a binary outcome’. Statistics in Medicine. Paper | R package on CRAN

Loh, W. W., Richardson, T. S., & Robins, J. M. (2017). An Apparent Paradox Explained. Statistical Science. Paper

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