59 citations · 153 across the 23 of their papers we have counts for
23 papers
Evaluating the effects of policy interventions subject to early adoption: A case study of prescription drug monitoring programs and opioid dispensing
Sarika Aggarwal, Brent A. Coull, Nima Hejazi +1
Policies that require organizations to use new systems, such as prescription drug monitoring programs (PDMPs), are often implemented in phases, with an initial period of voluntary…
Evaluating causal indirect effects when mediators are left-censored by assay limit of quantification
Cong Jiang, Michael D. Hughes, Nima S. Hejazi
Causal mediation analysis is essential for disentangling the mechanisms by which investigational therapeutic and preventive agents impact clinical outcomes. However, the measuremen…
A Riesz Representer Perspective on Targeted Learning
Salvador V. Balkus, Christian Testa, Nima S. Hejazi
As research in causal inference has sought to address more complex scientific questions, the number of specialized estimands in the field has proliferated. Recognition that many of…
Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
Philippe Boileau, Nima S. Hejazi, Ivana Malenica +3
Causal mediation analysis provides techniques for defining and estimating effects that may be endowed with mechanistic interpretations. With many scientific investigations seeking…
On the use of cross-fitting in causal machine learning with correlated units
Salvador V. Balkus, Hasan Laith, Nima S. Hejazi
In causal machine learning, the fitting and evaluation of nuisance models are often performed on separate partitions, or folds, of the observed data. This technique, called cross-f…
Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
Keith Barnatchez, Kevin P. Josey, Nima S. Hejazi +3
Measurement error is a common challenge for causal inference studies using electronic health record (EHR) data, where clinical outcomes and treatments are frequently mismeasured. R…
The causal effects of modified treatment policies under network interference
Salvador V. Balkus, Scott W. Delaney, Nima S. Hejazi
Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal eff…
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…
A nonparametric framework for treatment effect modifier discovery in high dimensions
Philippe Boileau, Ning Leng, Nima S. Hejazi +2
Heterogeneous treatment effects are driven by treatment effect modifiers, pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncov…
Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning
Nima S. Hejazi, Mark J. van der Laan
About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing cau…
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…
Causal survival analysis under competing risks using longitudinal modified treatment policies
Iván Díaz, Katherine L Hoffman, Nima S. Hejazi
Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatmen…
A framework for causal segmentation analysis with machine learning in large-scale digital experiments
Nima S. Hejazi, Wenjing Zheng, Sathya Anand
We present an end-to-end methodological framework for causal segment discovery that aims to uncover differential impacts of treatments across subgroups of users in large-scale digi…
Evaluating the Robustness of Targeted Maximum Likelihood Estimators via Realistic Simulations in Nutrition Intervention Trials
Haodong Li, Sonali Rosete, Jeremy Coyle +9
Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, dou…
Cross-Validated Loss-Based Covariance Matrix Estimator Selection in High Dimensions
Philippe Boileau, Nima S. Hejazi, Mark J. van der Laan +1
The covariance matrix plays a fundamental role in many modern exploratory and inferential statistical procedures, including dimensionality reduction, hypothesis testing, and regres…
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…
Targeting Learning: Robust Statistics for Reproducible Research
Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica +9
Targeted Learning is a subfield of statistics that unifies advances in causal inference, machine learning and statistical theory to help answer scientifically impactful questions w…
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
Ashkan Ertefaie, Nima S. Hejazi, Mark J. van der Laan
Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biase…
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…
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…
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…
A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology
Nima S. Hejazi, Philippe Boileau, Mark J. van der Laan +1
The widespread availability of high-dimensional biological data has made the simultaneous screening of many biological characteristics a central problem in computational biology an…
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings
Weixin Cai, Nima S. Hejazi, Alan E. Hubbard
Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypothes…