The Highly Adaptive Lasso Estimator

Last updated on Fri, Oct 23, 2020
causal-inference machine-learning targeted-learning
Nima Hejazi
Nima Hejazi
Assistant Professor of Biostatistics

My research lies at the intersection of causal inference and machine learning, developing flexible methodology for statistical inference tailored to modern experiments and observational studies in the biomedical and public health sciences.

Related

Publications

haldensify: Highly adaptive lasso conditional density estimation in R

The haldensify R package serves as a toolbox for nonparametric conditional density estimation based on the highly adaptive lasso, a …
Nima Hejazi, Mark van der Laan, David Benkeser
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Nonparametric inverse-probability-weighted estimators based on the highly adaptive lasso

Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of …
Ashkan Ertefaie, Nima Hejazi, Mark van der Laan
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Efficient estimation of modified treatment policy effects based on the generalized propensity score

Continuous treatments have posed a significant challenge for causal inference, both in the formulation and identification of …
Nima Hejazi, David Benkeser, Iván Díaz, Mark van der Laan
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Semiparametric statistical methods for causal inference with stochastic treatment regimes

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hal9001: Highly adaptive lasso regression in R

The hal9001 R package provides a computationally efficient implementation of the highly adaptive lasso, a flexible nonparametric …
Nima Hejazi, Jeremy Coyle, Mark van der Laan
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Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials

The advent and subsequent widespread availability of preventive vaccines has altered the course of public health over the past century. …
Nima Hejazi, Mark van der Laan, Holly Janes, Peter Gilbert, David Benkeser
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Talks

Efficient Estimation of Modified Treatment Policy Effects Based on the Generalized Propensity Score

Continuous treatment variables have posed a significant challenge for causal inference, both in the formulation and identification of …
Wed, Nov 17, 2021 10:30 AM New York, New York, United States (remote due to COVID-19)
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Nonparametric Estimation of the Generalized Propensity Score Based on the Highly Adaptive Lasso

Continuous treatment variables have posed a significant challenge for causal inference, both in the formulation and identification of …
Wed, May 19, 2021 6:30 AM Oslo, Norway (remote due to COVID-19)
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