Preprints and Articles under Review

  1. 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]

  2. Rudolph, K.E., and Díaz, Iván. Efficiently transporting causal (in) direct effects to new populations under intermediate confounding and with multiple mediators. arXiv preprint arXiv:2006.07708 (2020). [link]

  3. Díaz, Iván, Williams, N., Hoffman, K. L. & Schenck, E. J. Non-parametric causal effects based on longitudinal modified treatment policies 2020. arXiv: 2006.01366 [stat.ME]. [software] [link]

  4. 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]

Research Articles in Statistics and Epidemiology

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

  2. Díaz, Iván, Hejazi, N. S., Rudolph, K. E. & van der Laan, M. J. Non-parametric efficient causal mediation with intermediate confounders. To appear in Biometrika (2021). [software] [link]

  3. 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]

  4. 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]

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

  6. 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]

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

  8. 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]

  9. 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]

  10. 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]

  11. 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]

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

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

  14. 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]

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

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

  17. 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]

  18. 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]

  19. 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]

  20. 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]

  21. 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]

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

  23. 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]

  24. 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).

Book Chapters

  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).

Selected Publications on Clinical and Health Services Research
(see CV for a full list)
  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).