Iván Díaz | DeepAI

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∙ 05/11/2023

Causal Inference for Continuous Multiple Time Point Interventions

There are limited options to estimate the treatment effects of variables...

0 Michael Schomaker, et al. ∙

∙ 04/19/2023

Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures

This tutorial discusses a recently developed methodology for causal infe...

0 Katherine L. Hoffman, et al. ∙

∙ 03/31/2023

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

We develop flexible and nonparametric estimators of the average treatmen...

0 Kara E Rudolph, et al. ∙

∙ 03/01/2023

An end-to-end SE(3)-equivariant segmentation network

Convolutional neural networks (CNNs) allow for parameter sharing and tra...

0 Iván Díaz, et al. ∙

∙ 12/15/2022

Nonparametric estimators of interventional (transported) direct and indirect effects that accommodate multiple mediators and multiple intermediate confounders

Mediation analysis is appealing for its ability to improve understanding...

0 Kara E Rudolph, et al. ∙

∙ 11/18/2022

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

There is a long-standing debate in the statistical, epidemiological and ...

0 Kara E Rudolph, et al. ∙

∙ 08/10/2022

Heterogeneity assessment in causal data fusion problems

Previous works have formalized the conditions under which findings from ...

0 Tat-Thang Vo, et al. ∙

∙ 05/16/2022

Causal influence, causal effects, and path analysis in the presence of intermediate confounding

Recent approaches to causal inference have focused on the identification...

0 Iván Díaz, et al. ∙

∙ 05/11/2022

Efficient estimation of modified treatment policy effects based on the generalized propensity score

Continuous treatments have posed a significant challenge for causal infe...

0 Nima S. Hejazi, et al. ∙

∙ 05/09/2022

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

Natural direct and indirect effects are mediational estimands that decom...

0 Kara E Rudolph, et al. ∙

∙ 03/28/2022

Efficient and flexible causal mediation with time-varying mediators, treatments, and confounders

Interventional effects have been proposed as a solution to the unidentif...

0 Iván Díaz, et al. ∙

∙ 02/07/2022

Causal survival analysis under competing risks using longitudinal modified treatment policies

Longitudinal modified treatment policies (LMTP) have been recently devel...

0 Iván Díaz, et al. ∙

∙ 12/27/2021

Causal mediation with instrumental variables

Mediation analysis is a strategy for understanding the mechanisms by whi...

0 Kara E Rudolph, et al. ∙

∙ 09/09/2021

Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19

The rapid finding of effective therapeutics requires the efficient use o...

0 Nicholas Williams, et al. ∙

∙ 05/06/2021

When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws

Background: All states in the US have enacted at least some naloxone acc...

0 Kara E Rudolph, et al. ∙

∙ 03/03/2021

Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID-19 vaccine correlates of protection

Combating the SARS-CoV2 pandemic will require the fast development of ef...

0 David Benkeser, et al. ∙

∙ 01/21/2021

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

There is a growing literature on finding so-called optimal treatment rul...

0 Kara E Rudolph, et al. ∙

∙ 09/14/2020

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Causal mediation analysis has historically been limited in two important...

0 Nima S. Hejazi, et al. ∙

∙ 06/13/2020

Efficiently transporting causal (in)direct effects to new populations under intermediate confounding and with multiple mediators

The same intervention can produce different effects in different sites. ...

0 Kara E Rudolph, et al. ∙

∙ 06/02/2020

Non-parametric causal effects based on longitudinal modified treatment policies

Most causal inference methods consider counterfactual variables under in...

0 Iván Díaz, et al. ∙

∙ 12/20/2019

Non-parametric efficient causal mediation with intermediate confounders

Interventional effects for mediation analysis were proposed as a solutio...

0 Iván Díaz, et al. ∙

∙ 11/22/2019

Non-parametric targeted Bayesian estimation of class proportions in unlabeled data

We introduce a novel Bayesian estimator for the class proportion in an u...

0 Iván Díaz, et al. ∙

∙ 01/09/2019

Causal mediation analysis for stochastic interventions

Mediation analysis in causal inference has traditionally focused on bina...

0 Iván Díaz, et al. ∙

∙ 07/24/2018

Collaborative double robustness using the e-score

Estimation of causal parameters from observational data requires complet...

0 Iván Díaz, et al. ∙

∙ 09/01/2017

Statistical Inference for Machine Learning Inverse Probability Weighting with Survival Outcomes

We present an inverse probability weighted estimator for survival analys...

0 Iván Díaz, et al. ∙

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