26 citations · 46 across the 13 of their papers we have counts for
14 papers
Fair Risk Minimization under Causal Path-Specific Effect Constraints
Razieh Nabi, David Benkeser
This paper introduces a framework for estimating fair optimal predictions using machine learning where the notion of fairness can be quantified using path-specific causal effects.…
Monte Carlo Integration in Simple and Complex Simulation Designs
Ashley I. Naimi, David Benkeser, Jacqueline E. Rudolph
Simulation studies are used to evaluate and compare the properties of statistical methods in controlled experimental settings. In most cases, performing a simulation study requires…
Nonparametric Motion Control in Functional Connectivity Studies in Children with Autism Spectrum Disorder
Jialu Ran, Sarah Shultz, Benjamin B. Risk +1
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition associated with difficulties with social interactions, communication, and restricted or repetitive behaviors. To ch…
Statistical learning for constrained functional parameters in infinite-dimensional models
Razieh Nabi, Nima S. Hejazi, Mark J. van der Laan +1
We develop a general framework for estimating function-valued parameters under equality or inequality constraints in infinite-dimensional statistical models. Such constrained learn…
Flexible Nonparametric Inference for Causal Effects under the Front-Door Model
Anna Guo, David Benkeser, Razieh Nabi
Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed c…
Efficient estimation of modified treatment policy effects based on the generalized propensity score
Nima S. Hejazi, David Benkeser, Iván Díaz +1
Continuous treatments have posed a significant challenge for causal inference, both in the formulation and identification of scientifically meaningful effects and in their robust e…
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…
Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials
Nima S. Hejazi, Mark J. van der Laan, Holly E. Janes +2
The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. Despite this success, effective vaccines to…
Nonparametric inference for interventional effects with multiple mediators
David Benkeser
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total i…
Efficient Estimation of Pathwise Differentiable Target Parameters with the Undersmoothed Highly Adaptive Lasso
Mark J. van der Laan, David Benkeser, Weixin Cai
We consider estimation of a functional parameter of a realistically modeled data distribution based on observing independent and identically distributed observations. We define an…
Design and analysis considerations for a sequentially randomized HIV prevention trial
David Benkeser, Keith Horvath, Cathy Reback +2
TechStep is a randomized trial of a mobile health interventions targeted towards transgender adolescents. The interventions include a short message system, a mobile-optimized web a…
A nonparametric super-efficient estimator of the average treatment effect
David Benkeser, Weixin Cai, Mark J van der Laan
Doubly robust estimators of causal effects are a popular means of estimating causal effects. Such estimators combine an estimate of the conditional mean of the outcome given treatm…
Robust inference on the average treatment effect using the outcome highly adaptive lasso
Cheng Ju, David Benkeser, Mark J. van der Laan
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize fl…
A machine learning-based approach for estimating and testing associations with multivariate outcomes
David Benkeser, Andrew Mertens, Benjamin F. Arnold +5
We propose a method for summarizing the strength of association between a set of variables and a multivariate outcome. Classical summary measures are appropriate when linear relati…