8 citations · 9 across the 3 of their papers we have counts for
6 papers
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…
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…
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…