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Preprints and Articles under Review

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  1. Williams, N., Rosenblum, M. & Iván Díaz. "Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19" arXiv preprint arXiv:2109.04294 (2021). [link]

  2. Benkeser, David, Iván Díaz, and Jialu Ran. "Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID-19 vaccine correlates of protection." arXiv preprint arXiv:2103.02643 (2021). [link]

  3. Hejazi, N.S., Rudolph, K.E., van der Laan, M., and Díaz, Iván. "Nonparametric causal mediation analysis for stochastic interventional (in) direct effects." arXiv preprint arXiv:2009.06203 (2020). [link]

  4. Rudolph, Kara E., Catherine Gimbrone, Ellicott C. Matthay, Ivan Diaz, Corey S. Davis, John R. Pamplin II, Katherine Keyes, and Magdalena Cerda. "When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws." arXiv preprint arXiv:2105.02757 (2021). [link]

  5. Rudolph, K. E. & Díaz, Iván. When the ends don’t justify the means: Learning a treatment strategy to prevent harmful indirect effects. arXiv preprint arXiv:2101.08590 (2021). [link]

  6. Ogburn, E. L., Sofrygin, O., Díaz, Iván & van der Laan, M. J. Causal inference for social network data. arXiv preprint arXiv:1705.08527 (2019). [link]

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Research Articles in Statistics and Epidemiology

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  1. Díaz, Iván, Nicholas Williams, Katherine L. Hoffman, and Edward J. Schenck. "Nonparametric causal effects based on longitudinal modified treatment policies." Journal of the American Statistical Association (2021): 1-16. [software] [link]

  2. Rudolph, K.E., and Díaz, Iván. Efficiently transporting causal (in) direct effects to new populations under intermediate confounding and with multiple mediators. Biostatistics (2020). [link]

  3. Díaz, I., Hejazi, N. S., Rudolph, K. E., & van Der Laan, M. J. (2021). Nonparametric efficient causal mediation with intermediate confounders. Biometrika, 108(3), 627-641. [software] [link]

  4. Benkeser, D, Díaz, Iván, Luedtke, A, Segal, J, Scharfstein, D, Rosenblum, M. Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes. Biometrics. (2020).[link]

  5. Díaz, Iván. Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning. Biostatistics 21, 353–358 (2020). [link]

  6. Díaz, Iván, Savenkov, O. & Kamel, H. Non-parametric targeted Bayesian estimation of class proportions in unlabeled data. Biostatistics (2020). [software] [link]

  7. Díaz, Iván & Hejazi, N. S. Causal mediation analysis for stochastic interventions. Journal of the Royal Statistical Society: Series B (Statistical Methodology) n/a. doi:10.1111/rssb.12362 (2020). [software] [link]

  8. Díaz, Iván. Statistical inference for data-adaptive doubly robust estimators with survival outcomes. Statistics in Medicine 38, 2735–2748 (2019). [software] [link]

  9. Díaz, Iván, Savenkov, O. & Ballman, K. Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes. Biometrika 105, 723–738 (2018). [link]

  10. Díaz, Iván, Colantuoni, E., Hanley, D. F. & Rosenblum, M. Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards. Lifetime Data Analysis. ISSN: 1572-9249 (Feb. 2018). [software] [link]

  11. Díaz, Iván & van der Laan, M. J. Doubly robust inference for targeted minimum loss-based estimation in randomized trials with missing outcome data. Statistics in Medicine. ISSN: 1097-0258. doi:10.1002/sim.7389 (2018). [link]

  12. Scharfstein, D., McDermott, A., Díaz, Iván, Carone, M., Lunardon, N. & Turkoz, I. Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach. Biometrics 74, 207–219 (2018). [link]

  13. Díaz, Iván. Efficient estimation of quantiles in missing data models. Journal of Statistical Planning and Inference 190 (2017): 39-51. [software] [link]

  14. Díaz, Iván, Colantuoni, E. & Rosenblum, M. Enhanced precision in the analysis of randomized trials with ordinal outcomes. Biometrics 72, 422 (2016). [software] [link]

  15. Díaz, Iván, Carone, M. & van der Laan, M. J. Second-Order Inference for the Mean of a Variable Missing at Random. The International Journal of biostatistics 12, 333–349 (2016). [link]

  16. Díaz, Iván, Hubbard, A., Decker, A. & Cohen, M. Variable Importance and Prediction Methods for Longitudinal Problems with Missing Variables. PloS ONE 10 (2015). [link]

  17. Díaz, Iván & Rosenblum, M. Targeted Maximum Likelihood Estimation using Exponential Families. International Journal of Biostatistics 11, 233–251 (2015). [link]

  18. Frangakis, C. E., Qian, T., Wu, Z. & Díaz, Iván. Deductive derivation and turing-computerization of semiparametric efficient estimation. Biometrics 71 (with discussion), 867–874 (2015). [link]

  19. Rudolph, K. E., Díaz, Iván, Rosenblum, M. & Stuart, E. A. Estimating Population Treatment Effects From a Survey Subsample. American Journal of Epidemiology 180, 737–748 (2014). [link]

  20. Díaz, Iván & van der Laan, M. J. Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions. The international journal of biostatistics 9, 161–174 (2013). [link]

  21. Díaz, Iván & van der Laan, M. J. Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems. The international journal of biostatistics 9, 149–160 (2013). [link]

  22. Díaz, Iván & van der Laan, M. J. Targeted Data Adaptive Estimation of the Causal Dose–Response Curve. Journal of Causal Inference 1, 171–192 (2013). [link]

  23. Díaz, Iván, and Mark van der Laan. Population intervention causal effects based on stochastic interventions. Biometrics 68.2 (2012): 541-549. [software] [link]​

  24. Díaz, Iván & van der Laan, M. J. Super Learner Based Conditional Density Estimation With Application to Marginal Structural Models. The International Journal of Biostatistics 7, 1–20 (2011). [link]

  25. Cepeda-Cuervo, E., Aguilar, W., Cervantes, V., Corrales, M., Díaz, Iván & Rodríguez, D. Intervalos de confianza e intervalos de credibilidad para una proporción. Revista Colombiana de Estadística 31, 211–228 (2008).

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Book Chapters

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  1. Carone, M., Díaz, Iván & van der Laan, M. J. in Targeted Learning in Data Science 483–510 (Springer, 2018).

  2. Díaz, Iván, Luedtke, A. R. & van der Laan, M. J. in Targeted Learning in Data Science 511–522 (Springer, 2018).

  3. Díaz, Iván & van der Laan, M. J. in Targeted Learning in Data Science 219–232 (Springer, 2018).

  4. Díaz, Iván. in Handbook on Big Data (eds van der Laan, M. J., Buhlman, P., Kane, M. & Drineas, P.) (Chapman and Hall, 2016).

  5. Díaz, Iván, Hubbard, A. & van der Laan, M. in Targeted Learning (eds van der Laan, M. J. & Rose, S.) (Springer, 2011).

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Selected Publications on Clinical and Health Services Research
(see CV for a full list)
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  1. Rudolph, K., Díaz, Iván, Hejazi, N., van der Laan, M., Luo, S., Shulman, M., Campbell, A., Rotrosen, J., and Nunes, E. "Explaining differential effects of medication for opioid use disorder using a novel approach incorporating mediating variables." Addiction (2020).

  2. Kummer, B. R., Díaz, Iván, Wu, X., Aaroe, A. E., Chen, M. L., Iadecola, C., Kamel, H. & Navi, B. B. Associations between cerebrovascular risk factors and parkinson disease. Annals of neurology 86,572–581 (2019).

  3. Murthy, S., Díaz, Iván, Wu, X., Merkler, A., Iadecola, C., Navi, B. B. & Kamel, H. Intracerebral Hemorrhage and Increased Risk of Arterial Ischemic Events in Annals of Neurology 86 (2019), S259–S260.

  4. Mosconi, L., Rahman, A., Díaz, Iván, Wu, X., Scheyer, O., Hristov, H. W., Vallabhajosula, S., Isaacson, R. S., de Leon, M. J. & Brinton, R. D. Increased Alzheimer’s risk during the menopause transition: A 3-year longitudinal brain imaging study. PloS one 13 (2018).

  5. Kreif, N., Grieve, R., Díaz, Iván & Harrison, D. Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury. Health economics 24, 1213–1228 (2015).

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