Publications by Methodologic Area

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Mediation Analysis - Direct and Indirect Effects


VanderWeele, T.J. (2014). A unification of mediation and interaction: a four-way decomposition. Epidemiology, in press.

VanderWeele, T.J. (2013). A three-way decomposition of a total effect into direct, indirect, and interactive effects. Epidemiology, 24:224-232.

VanderWeele, T.J. (2012). Mediation analysis with multiple versions of the mediator. Epidemiology, 23:454-463.


VanderWeele, T.J., Vansteelandt, S., and Robins, J.M. (2014). Methods for effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology, 25:300-306.

VanderWeele, T.J. and Vansteelandt, S. (2013). Mediation analysis with multiple mediators. Epidemiologic Methods, 2:95-115.

Valeri, L. and VanderWeele, T.J. (2013). Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods, 18:137-150.

VanderWeele, T.J. (2011). Causal mediation analysis with survival data. Epidemiology, 22:582-585.

VanderWeele, T.J. and Vansteelandt, S. (2010). Odds ratios for mediation analysis with a dichotomous outcome. American Journal of Epidemiology, 172:1339-1348.

VanderWeele, T.J. and Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface 2:457-468.

VanderWeele, T.J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology, 20:18-26.

  Sensitivity Analysis

VanderWeele, T.J. and Chiba, Y. (2014). Sensitivity analysis for direct and indirect effects in the presence of exposure-induced mediator-outcome confounders. Epidemiology, Biostatistics, and Public Health, in press.

VanderWeele, T.J. (2013). Unmeasured confounding and hazard scales: sensitivity analysis for total, direct and indirect effects. European Journal of Epidemiology, 28:113-117.

VanderWeele, T.J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21:540-551.

  Identification and Bounds

Tchetgen Tchetgen, E.J. and VanderWeele, T.J. (2014). On identification of natural direct effects when a confounder of the mediator is directly affected by exposure. Epidemiology, 25:282-291.

Vansteelandt, S. and VanderWeele, T.J. (2012). Natural direct and indirect effects on the exposed: effect decomposition under weaker assumptions. Biometrics, 68:1019-1027.

Hafeman, D.M. and VanderWeele, T.J. (2011). Alternative assumptions for the identification of direct and indirect effects. Epidemiology, 22:753-764.

Shpitser, I. and VanderWeele, T.J. (2011). A complete graphical criterion for the adjustment formula in mediation analysis. International Journal of Biostatistics, 7, Article 16:1-24.

VanderWeele, T.J. (2011). Controlled direct and mediated effects: definition, identification and bounds. Scandinavian Journal of Statistics, 38:551-563.

  Relations with Other Concepts Concerning Intermediates

VanderWeele, T.J., Tchetgen Tchetgen E.J., Cornelis, M., and Kraft, P. (2014). Methodological challenges with Mendelian randomization analysis. Epidemiology, 25:427-435.

VanderWeele, T.J. (2013). Surrogate measures and consistent surrogates (with Discussion). Biometrics, 69:561-681.

VanderWeele, T.J. (2012). Should principal stratification be used to study mediational processes? Journal of Research on Educational Effectiveness, 5:245-249.

VanderWeele, T.J. (2011). Principal stratification: uses and limitations. International Journal of Biostatistics, 7, Article 28:1-14.

VanderWeele, T.J. (2008). Simple relations between principal stratification and direct and indirect effects. Statistics and Probability Letters, 78:2957-2962.

Spillover Effects and Interference

  Social Networks

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. (2013). Inference for influence over multiple degrees of separation on a social network. Statistics in Medicine, 32:591-596.

VanderWeele, T.J., Ogburn, E.L. and Tchetgen Tchetgen, E.J. (2012). Why and when "flawed" social network analyses still yield valid tests of no contagion. Statistics, Politics, and Policy, 3, Article 4:1-11.

VanderWeele, T.J. (2011). Sensitivity analysis for contagion effects in social networks. Sociological Methods and Research, 40:240-255.

  Spillover Effects Using Covariate Control

Ogburn, E.L. and VanderWeele, T.J., Causal diagrams for interference and contagion. Technical Report.

VanderWeele, T.J., Tchetgen Tchetgen E.J., and Halloran, M.E. Interference and sensitivity analysis. Statistical Science, in press.

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. (2010). Direct and indirect effects for neighborhood-based clustered and longitudinal data. Sociological Methods and Research, 38:515-544.

VanderWeele, T.J. (2008). Ignorability and stability assumptions in neighborhood effects research. Statistics in Medicine, 27:1934-1943.

  Spillover Effects Under Individual Randomization

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.

VanderWeele, T.J., Tchetgen Tchetgen, E.J., and Halloran, M.E. (2012). Components of the indirect effect in vaccine trials: identification of contagion and infectiousness effects. Epidemiology, 23:751-761.

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.

VanderWeele, T.J. and Tchetgen Tchetgen, E.J. (2011). Bounding the infectiousness effect in vaccine trials. Epidemiology, 22:686-693.

VanderWeele, T.J. and Tchetgen Tchetgen, E.J. (2011). Effect partitioning under interference for two-stage randomized vaccine trials. Statistics and Probability Letters, 81:861-869.

Mechanistic Interaction


Robins, J.M., VanderWeele, T.J., and Gill, R. (2014). A proof of Bell's inequality in quantum mechanics using causal interactions. Scandinavian Journal of Statistics, in press.

Suzuki, E. and VanderWeele, T.J. (2014). Compositional epistasis: an epidemiologic perspective. In: Moore, J. (Ed.) Epistasis. Springer, in press.

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. and Robins, J.M. (2012). Stochastic counterfactuals and stochastic sufficient causes. Statistica Sinica, 22:379-392.

VanderWeele, T.J., Chen, Y. and Ahsan, H. (2011). Inference for causal interactions for continuous exposures under dichotomization. Biometrics, 67:1414-1421.

VanderWeele, T.J. (2010). Epistatic interactions. Statistical Applications in Genetics and Molecular Biology, 9, Article 1:1-22.

VanderWeele, T.J. (2010). Attributable fractions for sufficient cause interactions. International Journal of Biostatistics, 10(2), Article 5:1-26.

VanderWeele, T.J. (2010). Sufficient cause interactions for categorical and ordinal exposures with three levels. Biometrika, 97:647-659.

VanderWeele, T.J. and Robins, J.M. (2008). Empirical and counterfactual conditions for sufficient cause interactions. Biometrika, 95:49-61.


VanderWeele, T.J. and Vansteelandt, S. (2014). Some advantages of RERI - towards better estimators of additive interaction. American Journal of Epidemiology, in press.

Vansteelandt, S., VanderWeele, T.J. and Robins, J.M. (2012). Semiparametric tests for sufficient cause interactions. Journal of the Royal Statistical Society, Series B, 74:223-244.

VanderWeele, T.J. and Vansteelandt, S. (2011). A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models. American Journal of Epidemiology, 174:1197-1203.

VanderWeele, T.J. (2011). Causal interactions in the proportional hazards model. Epidemiology, 22:713-717.

VanderWeele, T.J. and Laird, N.M. (2011). Tests for compositional epistasis under single interaction-parameter models. Annals of Human Genetics, Special Issue on Epistasis, 75:146-156.

VanderWeele, T.J., Hernández-Diaz, S. and Hernán, M.A. (2010). Case-only gene-environment interaction studies: when does association imply mechanistic interaction? Genetic Epidemiology, 34:327-334.

VanderWeele, T.J., Vansteelandt, S. and Robins, J.M. (2010). Marginal structural models for sufficient cause interactions. American Journal of Epidemiology, 171:506-514.

Vansteelandt, S., VanderWeele, T.J., Tchetgen, E.J., Robins, J.M., (2008). Multiply robust inference for statistical interactions. Journal of the American Statistical Association, 103:1693-1704.

Interaction and Effect Modification


VanderWeele, T.J. and Knol, M.J. (2011). Remarks on antagonism. American Journal of Epidemiology, 173:1140-1147.

VanderWeele, T.J. (2010). Empirical tests for compositional epistasis. Nature Reviews Genetics, 11:166.

VanderWeele, T.J. (2009). On the distinction between interaction and effect modification. Epidemiology, 20:863-871.

VanderWeele, T.J. (2009). Sufficient cause interactions and statistical interactions. Epidemiology, 20:6-13.

VanderWeele, T.J. and Robins, J.M. (2007). Four types of effect modification - a classification based on directed acyclic graphs. Epidemiology, 18:561-568.

VanderWeele, T.J. and Robins, J.M. (2007). The identification of synergism in the sufficient-component cause framework. Epidemiology, 18:329-339.

  Sensitivity Analysis and Robustness to Unmeasured Confounding

Tchetgen Tchetgen, E.J. and VanderWeele, T.J. Robustness of measures of interaction to unmeasured confounding. Technical Report.

VanderWeele, T.J. Ko, Y-.A., and Mukherjee, B. (2013). Environmental confounding in gene-environment interaction studies. American Journal of Epidemiology, 178:144-152.

VanderWeele, T.J., Mukherjee, B. and Chen, J. (2012). Sensitivity analysis for interactions under unmeasured confounding. Statistics in Medicine, 31:2552-2564.

  Effect Attribution

VanderWeele, T.J. and Tchetgen Tchetgen, E.J. Attributing effects to interactions. Epidemiology, in press.

VanderWeele, T.J. (2013). Reconsidering the denominator of the attributable proportion for additive interaction. European Journal of Epidemiology, 28:779-784.

  Power and Sample Size

VanderWeele, T.J. (2012). Sample size and power calculations for additive interactions. Epidemiologic Methods, 1:159-188.

VanderWeele, T.J. (2011). Sample size and power calculations for case-only interaction studies. Epidemiology, 22:873-874.

  Guidelines and Tutorials

VanderWeele, T.J. and Knol, M.J. (2014). A tutorial on interaction. Epidemiologic Methods, in press.

Knol, M.J. and VanderWeele, T.J. (2012). Guidelines for presenting analyses of effect modification and interaction. International Journal of Epidemiology, 41:514-520.

Knol, M.J., VanderWeele, T.J., Groenwold, R.H.H., Klungel, O.H., Rovers, M.M., and Grobbee, D.E. (2011). Estimating measures of interaction on an additive scale for preventive exposures. European Journal of Epidemiology, 26:433-438.

VanderWeele, T.J. and Knol, M.J. (2011). The interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions. Annals of Internal Medicine, 154:680-683.


  Concepts and Confounder Selection

VanderWeele, T.J. and Shpitser, I. (2013). On the definition of a confounder. Annals of Statistics, 41:196-220.

VanderWeele, T.J. (2012). Confounding and effect modification: distribution and measure. Epidemiologic Methods, 1:55-82.

VanderWeele, T.J. and Shpitser, I. (2011). A new criterion for confounder selection. Biometrics, 67:1406-1413.

VanderWeele, T.J. (2010). Genetic self-knowledge and the future of epidemiologic confounding. American Journal of Human Genetics, 87(2):168-172.

VanderWeele, T.J. (2009). On the relative nature of over-adjustment and unnecessary adjustment. Epidemiology, 20:496-499.

  Sensitivity Analysis for Unmeasured Confounding

VanderWeele, T.J. and Arah, O.A. (2011). Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments and confounders. Epidemiology, 22:42-52.

VanderWeele, T.J. (2008). Sensitivity analysis: distributional assumptions and confounding assumptions. Biometrics, 64:645-649.

  Bounds for Unmeasured Confounding

Jiang, Z., Chiba, Y. and VanderWeele, T.J., Monotone confounding, monotone treatment selection, and monotone treatment response. Journal of Causal Inference, in press.

VanderWeele, T.J., Hernán, M.A. and Robins, J.M. (2008). Causal directed acyclic graphs and the direction of unmeasured confounding bias. Epidemiology, 19:720-728.

VanderWeele, T.J. (2008). The sign of the bias of unmeasured confounding. Biometrics, 64:702-706.

Measurement Error and Misclassification

Valeri, L. and VanderWeele, T.J., The estimation of direct and indirect causal effects in the presence of a misclassified binary mediator. Biostatistics, in press.

Ogburn, E.L. and VanderWeele, T.J. (2013). Bias attenuation results for nondifferentially mismeasured ordinal and coarsened confounders. Biometrika, 100:241-248.

Ogburn, E.L. and VanderWeele, T.J. (2012). Analytic results on the bias due to nondifferential misclassification of a binary mediator. American Journal of Epidemiology, 176:555-561.

Ogburn, E.L. and VanderWeele, T.J. (2012). On the nondifferential misclassification of a binary confounder. Epidemiology, 23:433-439.

Pierce, B.L. and VanderWeele, T.J. (2012). The effect of non-differential measurement error on bias, precision, and power in Mendelian randomization studies. International Journal of Epidemiology, 41:1383-1393.

VanderWeele, T.J., Valeri, L., and Ogburn, E.L. (2012). The role of misclassification and measurement error in mediation analyses. Epidemiology, 23:561-564.

VanderWeele, T.J. and Hernán, M.A. (2012). Results on differential and dependent measurement error of the exposure and the outcome using signed DAGs. American Journal of Epidemiology, 175:1303-1310.

VanderWeele, T.J. (2012). Inference for additive interaction under exposure misclassification. Biometrika, 99:502-508.

Causal Directed Acyclic Graphs - Theoretical Contributions

VanderWeele, T.J. and Tan, Z. (2012). Directed acyclic graphs with edge-specific bounds. Biometrika, 99:115-126.

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.

Shpitser, I., VanderWeele, T.J. and Robins, J.M. (2010). On the validity of covariate adjustment for estimating causal effects. Proceedings of the 26th Conference on Uncertainty and Artificial Intelligence, 527-536, AUAI Press: Corvallis, WA.

VanderWeele, T.J. and Robins, J.M. (2009). The properties of monotonic effects on directed acyclic graphs. Journal of Machine Learning Research - Special Topic on Causality, 10:699-718.

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 and Consistency

VanderWeele, T.J. and Hernán, M.A. (2013). Causal inference under multiple versions of treatment. Journal of Causal Inference, 1:1-20.

VanderWeele, T.J. and Hernán, M.A. (2012). Causal effects and natural laws: towards a conceptualization of causal counterfactuals for non-manipulable exposures with application to the effects of race and sex. Causal Inference: Statistical Perspectives and Applications, (C. Berzuini, P. Dawid and L. Bernardinelli, eds.). Wiley and Sons., p. 101-113.

Hernán, M.A. and VanderWeele, T.J. (2011). Compound treatments and transportability of causal inference. Epidemiology, 22:368-377.

VanderWeele, T.J. (2009). Concerning the consistency assumption in causal inference. Epidemiology, 20:880-883.

Epidemiologic Concepts

  Sufficient Causes

VanderWeele, T.J. (2012). The sufficient cause framework in statistics, philosophy and the biomedical and social sciences. Causal Inference: Statistical Perspectives and Applications, (C. Berzuini, P. Dawid and L. Bernardinelli, eds.). Wiley and Sons., Chapter 13, p. 180-191.

VanderWeele, T.J. (2009). Mediation and mechanism. European Journal of Epidemiology, 24:217-224.

VanderWeele, T.J. and Robins, J.M. (2007). Directed acyclic graphs, sufficient causes and the properties of conditioning on a common effect. American Journal of Epidemiology, 166:1096-1104.

VanderWeele, T.J. and Hernán, M.A. (2006). From counterfactuals to sufficient component causes, and vice versa. European Journal of Epidemiology, 21:855-858.

  Language and Epidemiologic Concepts

VanderWeele, T.J. (2011). Subtleties of explanatory language: what is meant by "mediation"? European Journal of Epidemiology, 26:343-346.

VanderWeele, T.J. (2011). A word and that to which it once referred: assessing "biologic" interaction. Epidemiology, 22:612-613.

VanderWeele, T.J. (2010). Response to "On the definition of effect modification," by E. Shahar and D.J. Shahar. Epidemiology, 21:587-588.


Huang, Y.-T., VanderWeele, T.J., and Lin, X. Joint analysis of SNP and gene expression data in genetic association studies of complex diseases. Annals of Applied Statistics, in press.

VanderWeele, T.J., and Emsley, R. (2013). Discussion of "Experimental design for identifying causal mechanisms. "Journal of the Royal Statistical Society, Series A, 176:46.

Sauer, B.C., Brookhart, M.A., Roy, J., and VanderWeele, T.J. (2013). Covariate selection. Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. Agency for Healthcare Research and Quality: Rockville, MD. Chapter 7, p. 93-108.

VanderWeele, T.J. (2012). Structural equation modeling in epidemiologic analysis. American Journal of Epidemiology, 176:608-612.

VanderWeele, T.J. and Ogburn, E.L. (2012). Theorems, proofs, examples and rules in the practice of epidemiology. Epidemiology, 23:443-445.

Mukherjee, B., Ko, Y.A., VanderWeele, T.J., Roy, A., Park, S.K., Chen, J. (2012). Principal interactions analysis for repeated measures data: application to gene-gene, gene-environment interactions. Statistics in Medicine, 31:2531-2551.

Chen, J., Kang, G., VanderWeele, T.J., Zhang, C., Mukherjee, B. (2012). Efficient designs of gene-environment interaction studies: implications of Hardy-Weinberg equilibrium and gene-environment independence. Statistics in Medicine, 31:2516-2530.

Pierce, B.L., Ahsan, H., and VanderWeele, T.J. (2011). Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. International Journal of Epidemiology, 40:740-752.

Chiba, Y. and VanderWeele, T.J. (2011). A simple method for principal strata effects when the outcome has been truncated due to death. American Journal of Epidemiology, 173:745-751.

VanderWeele, T.J. (2009). Criteria for the characterization of token causation. Logic and Philosophy of Science, 7:115-127.

VanderWeele, T.J. (2009). An extension of some instrumental variable results of Zhiqiang Tan. Supplemental material for: Journal of the American Statistical Association, 104:427.

VanderWeele, T.J. (2009). Conditional independence. In Kattan M.W., ed. Encyclopedia of Medical Decision Making. Thousand Oaks, CA: Sage Publications.

VanderWeele, T.J. (2008). Discussion of "Sampling bias and logistic models" by P. McCullagh. Journal of the Royal Statistical Society, Series B, 70:673-674.

Hudson, J.I., Javaras, K.N., Laird, N.M., VanderWeele, T.J., Pope, H.G. and Hernán, M.A. (2008). A structural approach to the familial coaggregation of disorders. Epidemiology, 19:431-439.

Robins, J.M., VanderWeele, T.J. and Richardson, T.S. (2006). Comment on: "Causal effects in the presence of non compliance: a latent variable interpretation" by A. Forcina. Metron International Journal of Statistics, 64:288-298.

VanderWeele, T.J. (2006). The use of propensity score methods in psychiatric research. International Journal of Methods in Psychiatric Research, 15:95-103.


Copies of papers are available upon request.

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