I. Díaz

发表

Stochastic Treatment Regimes

M. J. Laan, I. Díaz, 2018 .

Causal Inference for Social Network Data

Elizabeth L. Ogburn, M. J. van der Laan, E. Ogburn, 2017, Journal of the American Statistical Association.

Statistical Inference for Machine Learning Inverse Probability Weighting with Survival Outcomes

Iván Díaz, I. Díaz, 2017 .

Estimating population treatment effects from a survey subsample.

Kara E Rudolph, E. Stuart, M. Rosenblum, 2014, American journal of epidemiology.

Targeted Maximum Likelihood Estimation using Exponential Families

M. Rosenblum, I. Díaz, Michael Rosenblum, 2014, The international journal of biostatistics.

Targeted learning ensembles for optimal individualized treatment rules with time-to-event outcomes.

I. Díaz, O. Savenkov, K. Ballman, 2017, Biometrika.

Improved Efficiency in the Analysis of Randomized Trials with Survival Outcomes

M. Rosenblum, I. Díaz, E. Colantuoni, 2015 .

The Statistics of Sensitivity Analyses

Mark J. van der Laan, Alexander R. Luedtke, Iván Díaz, 2015 .

Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes

D. Scharfstein, M. Rosenblum, I. Díaz, 2020, medRxiv.

Improving precision and power in randomized trials for COVID‐19 treatments using covariate adjustment, for binary, ordinal, and time‐to‐event outcomes

D. Scharfstein, M. Rosenblum, I. Díaz, 2020, Biometrics.

Efficient and flexible estimation of natural mediation effects under intermediate confounding and monotonicity constraints

Kara E Rudolph, I. Díaz, K. Rudolph, 2022, 2205.04408.

Heterogeneity assessment in causal data fusion problems

Kara E Rudolph, I. Díaz, T. Vo, 2022, 2208.05543.

Optimising precision and power by machine learning in randomised trials with ordinal and time‐to‐event outcomes with an application to COVID‐19

M. Rosenblum, I. Díaz, Nicholas Williams, 2022, Journal of the Royal Statistical Society. Series A,.

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

Kara E. Rudolph, Caleb H. Miles, I. Díaz, 2022, 2211.10310.

Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards

I. Díaz, M. Rosenblum, E. Colantuoni, 2018, Lifetime Data Analysis.

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

Kara E. Rudolph, Caleb H. Miles, I. Díaz, 2022, 2211.10310.

Doubly robust inference for targeted minimum loss–based estimation in randomized trials with missing outcome data

Mark J van der Laan, Iván Díaz, M. J. van der Laan, 2017, Statistics in medicine.

Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning.

Iván Díaz, I. Díaz, 2019, Biostatistics.

Sensitivity Analysis for Causal Inference under Unmeasured Confounding and Measurement Error Problems

Mark J van der Laan, Iván Díaz, M. J. van der Laan, 2013, The international journal of biostatistics.

When Effects Cannot be Estimated: Redefining Estimands to Understand the Effects of Naloxone Access Laws.

Kara E. Rudolph, Ivan Diaz, Ellicott C. Matthay, 2021, Epidemiology.

Causal mediation with instrumental variables

Kara E. Rudolph, Ivan Diaz, Nicholas Williams, 2021, 2112.13898.

Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Ordinal or Time to Event Outcomes

D. Scharfstein, M. Rosenblum, I. Díaz, 2020 .

Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards

M. Rosenblum, D. Hanley, I. Díaz, 2015, Lifetime Data Analysis.

Enhanced precision in the analysis of randomized trials with ordinal outcomes

Michael Rosenblum, Elizabeth Colantuoni, Iván Díaz, 2016, Biometrics.

Collaborative double robustness using the $e$-score

I. Díaz, Iv'an D'iaz, 2018 .

Higher-Order Targeted Loss-Based Estimation

Mark J. van der Laan, Marco Carone, Iván Díaz, 2018 .

Efficient Estimation of Quantiles in Missing Data Models

I. Díaz, 2015, 1512.08110.

Discussion of Identification, Estimation and Approximation of Risk under Interventions that Depend on the Natural Value of Treatment Using Observational Data, by Jessica Young, Miguel Hernán, and James Robins

Mark J van der Laan, Alexander R Luedtke, Iván Díaz, 2014, Journal of causal inference.

Higher-order Targeted Minimum Loss-based Estimation

Mark J. van der Laan, Marco Carone, Iván Díaz, 2014 .

Targeted Data Adaptive Estimation of the Causal Dose–Response Curve

Iván Díaz, Mark J. van der Laan, M. J. van der Laan, 2013 .

Assessing the Causal Effect of Policies: An Example Using Stochastic Interventions

Mark J van der Laan, Iván Díaz, M. J. van der Laan, 2013, The international journal of biostatistics.

Assessing the Causal Effect of Policies: An Approach Based on Stochastic Interventions

M. J. Laan, I. Díaz, 2012 .

Efficiently transporting average treatment effects using a sufficient subset of effect modifiers

E. Stuart, I. Díaz, Nicholas T Williams, 2023, 2304.00117.

Helped into Harm

I. Díaz, C. Gimbrone, K. Rudolph, 2021, Epidemiology.

Statistical inference for data‐adaptive doubly robust estimators with survival outcomes

I. Díaz, 2017, Statistics in medicine.

Helped into Harm: Mediation of a Housing Voucher Intervention on Mental Health and Substance Use in Boys.

I. Díaz, C. Gimbrone, K. Rudolph, 2021, Epidemiology.

When the ends don't justify the means: Learning a treatment strategy to prevent harmful indirect effects

Kara E. Rudolph, Ivan Diaz, I. Díaz, 2021, 2101.08590.

A pr 2 02 3 Causal influence , causal effects , and path analysis in the presence of intermediate confounding

I. Díaz, 2023 .

Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes

D. Scharfstein, I. Díaz, D. Benkeser, 2020, medRxiv.

Global sensitivity analysis for repeated measures studies with informative drop‐out: A semi‐parametric approach

Marco Carone, Aidan McDermott, Nicola Lunardon, 2018, Biometrics.

Second-Order Inference for the Mean of a Variable Missing at Random

Marco Carone, Mark J van der Laan, Iván Díaz, 2015, The international journal of biostatistics.

Statistical inference for data‐adaptive doubly robust estimators with survival outcomes

I. Díaz, 2017, Statistics in medicine.

'medoutcon': Nonparametric efficient causal mediation analysis with machine learning in 'R'

Nima Hejazi, Iván Díaz, Kara Rudolph, 2022, J. Open Source Softw..

Deductive derivation and turing‐computerization of semiparametric efficient estimation

Tianchen Qian, Zhenke Wu, Constantine E Frangakis, 2015, Biometrics.