My research focuses on the development of statistical methods for causal inference from observational and randomized studies with complex datasets. I work at the intersection of statistical machine learning and optimal asymptotic estimation in large semi- and nonparametric models.  My substantive research includes applications in neurology, precision medicine for cancer, and physical activity. 

  • Díaz, Iván, Oleksandr Savenkov, and Karla Ballman. "Targeted Learning Ensembles for Optimal Individualized Treatment Rules with Time-to-Event Outcomes." Biometrika (In press).​

  • Dıaz, Iván, Colantuoni, Elizabeth, Hanley, Daniel. F, and Rosenblum, Michael. "Improved Precision in the Analysis of Randomized Trials with Survival Outcomes, without Assuming Proportional Hazards. Lifetime Data Analysis." (In Press)

  • Díaz, Iván. "Efficient estimation of quantiles in missing data models." Journal of Statistical Planning and Inference 190 (2017): 39-51.

  • Díaz, Iván, and Mark J. van der Laan. "Doubly robust inference for targeted minimum loss–based estimation in randomized trials with missing outcome data." Statistics In Medicine 36.24 (2017): 3807-3819.

  • Díaz, Iván, and Mark van der Laan. "Population intervention causal effects based on stochastic interventions." Biometrics 68.2 (2012): 541-549.

  • Díaz, Iván, Elizabeth Colantuoni, and Michael Rosenblum. "Enhanced precision in the analysis of randomized trials with ordinal outcomes." Biometrics 72.2 (2016): 422-431.​

  • Rudolph, Kara E., Iván Díaz, Michael Rosenblum, and Elizabeth A. Stuart. "Estimating Population Treatment Effects From a Survey Subsample." American journal of epidemiology 180, no. 7 (2014): 737-748.

  • Díaz, Iván, and Mark J. van der Laan. "Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems." The international journal of biostatistics 9, no. 2 (2013): 149-160.

  • Díaz, Iván, and Mark J. van der Laan. "Targeted data adaptive estimation of the causal dose–response curve." Journal of Causal Inference 1, no. 2 (2013): 171-192.

  • Díaz, Iván, Marco Carone, and Mark J. van der Laan. "Second-Order Inference for the Mean of a Variable Missing at Random." The international journal of biostatistics 12, no. 1 (2016): 333-349.

Selected Publications   


Book Chapters


  • Stochastic Treatment Regimes for Causal Inference. In: Targeted Learning in Data Science . I. Díaz (upcoming 2017). Ed. by M. J. van der Laan and S. Rose. Springer.

  • Sensitivity Analysis for Causal Inference. In: Targeted Learning in Data Science . I. Díaz, A. Luedtke, and M. J. van der Laan. (upcoming 2017). Ed. by M. J. van der Laan and S. Rose. Springer.

  • Estimator and Model Selection Using Cross-Validation. In: Handbook on Big Data. I. Díaz (2016). Ed. by M. J. van der Laan, P. Buhlman, M. Kane, and P. Drineas. Chapman and Hall.

  • Targeted Bayesian Learning. In: Targeted Learning. I. Díaz, A. Hubbard, and M. van der Laan (2011). Ed. by M. J. van der Laan and S. Rose. Springer.

Google Inc

My work at Google involved developing tools for causal inference from very large observational studies including online and offline activity.

Johns Hopkins Bloomberg School of Public Health

Postdoctoral fellow in the Department of Biostatistics. My research included the development of covariate adjusted estimators with efficiency guarantees with applications to the MISTIE II and CLEAR III trials on stroke.

University of California at Berkeley

PhD in Biostatistics under the direction of Dr. Mark van der Laan. My dissertation on causal inference for continuous exposures was awarded the Erich L. Lehmann to an outstanding dissertation in theoretical statistics. 

Universidad Nacional de Colombia

MS and undergraduate degree in Statistics. 

Outside of work I enjoy rock climbing, running, and outdoors activities in general. I was born and grew up in Bogotá, Colombia.