Methodological Publications

Measurement for Psychosocial Constructs

• VanderWeele, T.J. and Padgett. (2024). Novel psychometric indicator assessments: the relative excess correlation and associated matrices. Preprint available at: osf.io/us2h8
• VanderWeele, T.J. and Batty, C.J.K. (2023). On the dimensional indeterminacy of one-wave factor analysis under causal effects. Journal of Causal Inference 11: 20220074.
• VanderWeele, T.J. and Vansteelandt, S. (2022).  A statistical test to reject the structural interpretation of a latent factor model. Journal of the Royal Statistical Society, Series B, 84:2032-2054.
• VanderWeele, T.J. (2022). Constructed measures and causal inference: towards a new model of measurement for psychosocial constructs. Epidemiology, 33:141-151.

Outcome-Wide Studies

• VanderWeele, T.J., Mathur, M.B., and Chen, Y. (2020). The future of outcome-wide studies. Statistical Science, 35:479-483.
• VanderWeele, T.J., Mathur, M.B., and Chen, Y. (2020). Outcome-wide longitudinal designs for causal inference: a new template for empirical studies. Statistical Science, 35:437-466.
• VanderWeele, T.J. (2017). Outcome-wide epidemiology. Epidemiology, 28:399-402.

Confounding and Confounders

• VanderWeele, T.J. (2019). Principles of confounder selection. European Journal of Epidemiology, 34:211-219.
• VanderWeele, T.J. and Shpitser, I. (2013). On the definition of a confounder. Annals of Statistics, 41:196-220.
• VanderWeele, T.J. and Shpitser, I. (2011). A new criterion for confounder selection. Biometrics, 67:1406-1413.

Sensitivity Analysis

• Smith, L.H., Mathur, M. and VanderWeele, T.J. (2021). Multiple-bias sensitivity analysis using bounds. Epidemiology, 32:625-634.
• VanderWeele, T.J. and Li, Y. (2019). Simple sensitivity analysis for differential measurement error. American Journal of Epidemiology, 188:1823-1829.
• Smith, L.H. and VanderWeele, T.J. (2019). Bounding bias due to selection. Epidemiology, 30:509-516.
• VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167:268-274.

Meta-Analysis and Replication

• Mathur, M. and VanderWeele, T.J. (2022). Methods to address confounding and other biases in meta-analyses: review and recommendations. Annual Review of Public Health, 43:19-35.
• Mathur, M. and VanderWeele, T.J. (2020). New statistical metrics for multisite replication projects. Journal of the Royal Statistical Society, Series A, 183:1145–1166.
• Mathur, M. and VanderWeele, T.J. (2020). Sensitivity analyses for publication bias in meta-analyses. Journal of the Royal Statistical Society, Series C, 69:1091-1119.
• Mathur, M. and VanderWeele, T.J. (2020). Sensitivity analysis for unmeasured confounding in meta-analyses. Journal of the American Statistical Association, 115:163-172.
• Mathur, M. and VanderWeele, T.J. (2019). New metrics for meta-analyses of heterogeneous effects. Statistics in Medicine, 3:1336-1342.

Mediation Analysis

• VanderWeele, T.J. and Tchetgen Tchetgen, E.J. (2017). Mediation analysis with time-varying exposures and mediators. Journal of the Royal Statistical Society, Series B, 79:917-938.
• VanderWeele, T.J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. New York: Oxford University Press.
• VanderWeele, T.J. (2014). A unification of mediation and interaction: a four-way decomposition. Epidemiology, 25:749-761.
• VanderWeele, T.J. (2013). Surrogate measures and consistent surrogates (with Discussion). Biometrics, 69:561-681.

Mendelian Randomization

• Skrivankova V.W., Richmond, R.C., Woolf, B.A.R., Yarmolinsky, J., Davies, N.M., Swanson, S.A., VanderWeele, T.J., Higgins, J.P.T., Timpson, N.J., Dimou, N., Langenberg, C., Golub, R.M., Loder, E.W., Gallo, V., Tybjaerg-Hansen, A., Davey Smith, G., Egger, M., and Richards, J.B. (2021). Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization: The STROBE-MR Statement. JAMA, 326:1614-1621.
• Swanson, S.A. and VanderWeele, T.J. (2020). E-values for Mendelian randomization. Epidemiology, 31:e23-e24.
• VanderWeele, T.J., Tchetgen Tchetgen E.J., Cornelis, M., and Kraft, P. (2014). Methodological challenges in Mendelian randomization. Epidemiology, 25:427-435.

Methods for Racial Disparities

• Jackson, J.W. and VanderWeele, T.J. (2018). Decomposition analysis to identify intervention targets for reducing disparities. Epidemiology, 29:825-835.
• Jackson, J.W., Williams, D.R., and VanderWeele, T.J. (2016). Disparities at the intersection of marginalized groups. Social Psychiatry and Psychiatric Epidemiology, 51:1349-1359.
• VanderWeele, T.J. and Robinson, W.R. (2014). On the causal interpretation of race in regressions adjusting for confounding and mediating variables. Epidemiology, 25:473-484.

Interaction

• VanderWeele, T.J., Luedtke A.R., van der Laan, M.J., and Kessler, R.C. (2019). Selecting optimal subgroups for treatment using many covariates. Epidemiology, 30:334-341.
• VanderWeele, T.J. (2019). The interaction continuum. Epidemiology, 30:648-658.
• VanderWeele, T.J. and Richardson, T.S. (2012). General theory for interactions in sufficient cause models with dichotomous exposures. Annals of Statistics, 40:2128-2161.
• VanderWeele, T.J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20:863-871.

Social Networks

• VanderWeele, T.J. and Christakis, N.A. (2019). Network multipliers and public health. International Journal of Epidemiology, 48:1032-1037.
• VanderWeele, T.J. and An, W. (2013). Social networks and causal inference. Handbook of Causal Analysis for Social Research, S.L. Morgan (ed.). Springer, Chapter 17, p. 353-374.
• VanderWeele, T.J. (2011). Sensitivity analysis for contagion effects in social networks. Sociological Methods and Research, 40:240-255.

Spillover Effects

• VanderWeele, T.J., Hong, G., Jones, S. and Brown, J. (2013). Mediation and spillover effects in group-randomized trials: a case study of the 4R’s educational intervention. Journal of the American Statistical Association, 108:469-482.
• VanderWeele, T.J., Vandenbroucke, J.P., Tchetgen Tchetgen, E.J., and Robins, J.M. (2012). A mapping between interactions and interference: implications for vaccine trials. Epidemiology, 23:285-292.
• Tchetgen Tchetgen, E.J. and VanderWeele, T.J. (2012). On causal inference in the presence of interference. Statistical Methods in Medical Research – Special Issue on Causal Inference, 21:55-75.

Causal Diagrams

• Ogburn, E.L. and VanderWeele, T.J. (2014). Causal diagrams for interference and contagion. Statistical Science, 29:559-578.
• VanderWeele, T.J. and Robins, J.M. (2010). Signed directed acyclic graphs for causal inference. Journal of the Royal Statistical Society, Series B, 72:111-127.
• VanderWeele, T.J. and Robins, J.M. (2009). Minimal sufficient causation and directed acyclic graphs. Annals of Statistics, 37:1437-1465.

Multiple Versions of Treatment

• VanderWeele, T.J. (2018). On well-defined hypothetical interventions in the potential outcomes framework. Epidemiology, 29:e24–e25.
• VanderWeele, T.J. (2016). On causes, causal inference, and potential outcomes. International Journal of Epidemiology, 45:1809-1816.
• VanderWeele, T.J. and Hernán, M.A. (2013). Causal inference under multiple versions of treatment. Journal of Causal Inference, 1:1-20.