59 citations · 98 across the 6 of their papers we have counts for
8 papers
Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19
Nicholas Williams, Michael Rosenblum, Iván Díaz
The rapid finding of effective therapeutics requires the efficient use of available resources in clinical trials. The use of covariate adjustment can yield statistical estimates wi…
Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID-19 vaccine correlates of protection
David Benkeser, Iván Díaz, Jialu Ran
Combating the SARS-CoV2 pandemic will require the fast development of effective preventive vaccines. Regulatory agencies may open accelerated approval pathways for vaccines if an i…
Nonparametric causal mediation analysis for stochastic interventional (in)direct effects
Nima S. Hejazi, Kara E. Rudolph, Mark J. van der Laan +1
Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary treatments and static interventions, and (ii) dir…
Non-parametric causal effects based on longitudinal modified treatment policies
Iván Díaz, Nicholas Williams, Katherine L. Hoffman +1
Most causal inference methods consider counterfactual variables under interventions that set the treatment deterministically. With continuous or multi-valued treatments or exposure…
Non-parametric efficient causal mediation with intermediate confounders
Iván Díaz, Nima S. Hejazi, Kara E. Rudolph +1
Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confoun…
Non-parametric targeted Bayesian estimation of class proportions in unlabeled data
Iván Díaz, Oleksander Savenkov, Hooman Kamel
We introduce a novel Bayesian estimator for the class proportion in an unlabeled dataset, based on the targeted learning framework. Our procedure requires the specification of a pr…
Causal mediation analysis for stochastic interventions
Iván Díaz, Nima Hejazi
Mediation analysis in causal inference has traditionally focused on binary exposures and deterministic interventions, and a decomposition of the average treatment effect in terms o…
Doubly robust estimators for the average treatment effect under positivity violations: introducing the -score
Iván Díaz
Estimation of causal parameters from observational data requires complete confounder adjustment, as well as positivity of the propensity score for each treatment arm. There is ofte…