Recent Publications

Nima Hejazi, Kara Rudolph, Mark van der Laan, Iván Díaz (2022). Nonparametric causal mediation analysis for stochastic interventional (in)direct effects. In Biostatistics.

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Nima Hejazi, Kara Rudolph, Iván Díaz (2022). medoutcon: Nonparametric efficient causal mediation analysis with machine learning in R. In Journal of Open Source Software.

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Nima Hejazi, Wenjing Zheng, Sathya Anand (2021). A framework for causal segmentation analysis with machine learning in large-scale digital experiments. CODE@MIT.

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Nima Hejazi (2021). Semiparametric statistical methods for causal inference with stochastic treatment regimes. PhD dissertation, Graduate Division, University of California, Berkeley.

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Philippe Boileau, Nima Hejazi, Brian Collica, Mark van der Laan, Sandrine Dudoit (2021). cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R. In Journal of Open Source Software.

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Philippe Boileau, Nima Hejazi, Mark van der Laan, Sandrine Dudoit (2021). Cross-validated loss-based covariance matrix estimator selection in high dimensions.

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Iván Díaz, Nima Hejazi, Kara Rudolph, Mark van der Laan (2020). Non-parametric efficient causal mediation with intermediate confounders. In Biometrika.

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Nima Hejazi, David Benkeser (2020). txshift: Efficient estimation of the causal effects of stochastic interventions in R. In Journal of Open Source Software.

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Nima Hejazi, Jeremy Coyle, Mark van der Laan (2020). hal9001: Highly adaptive lasso regression in R. In Journal of Open Source Software.

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Nima Hejazi, Mark van der Laan, Holly Janes, Peter Gilbert, David Benkeser (2020). Efficient nonparametric inference on the effects of stochastic interventions under two-phase sampling, with applications to vaccine efficacy trials. In Biometrics.

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Jeremy Coyle(?), Nima Hejazi(?), Ivana Malenica(?), Rachael Phillips(?), Benjamin Arnold, Andrew Mertens, Jade Benjamin-Chung, Weixin Cai, Sonali Dayal, John Colford Jr., Alan Hubbard, Mark van der Laan (2020). Targeted Learning: Robust statistics for reproducible research.

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Ashkan Ertefaie, Nima Hejazi, Mark van der Laan (2020). Nonparametric inverse probability weighted estimators based on the highly adaptive lasso.

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Philippe Boileau, Nima Hejazi, Sandrine Dudoit (2020). Exploring high-dimensional biological data with sparse contrastive principal component analysis. In Bioinformatics.

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Philippe Boileau, Nima Hejazi, Sandrine Dudoit (2020). scPCA: A toolbox for sparse contrastive principal component analysis in R. In Journal of Open Source Software.

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Iván Díaz, Nima Hejazi (2020). Causal mediation analysis for stochastic interventions. In Journal of the Royal Statistical Society, Series B (Statistical Methodology).

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Nima Hejazi, Philippe Boileau, Mark van der Laan, Alan Hubbard (2019). A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology.

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Amin Sobh, Alex Loguinov, Gulce Naz Yazici, Rola Zeidan, Abderrahmane Tagmount, Nima Hejazi, Alan Hubbard, Luoping Zhang, Chris Vulpe (2019). Functional profiling identifies determinants of arsenic trioxide cellular toxicity. In Toxicological Sciences.

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Weixin Cai, Alan Hubbard, Nima Hejazi (2018). adaptest: Data-adaptive statistics for high-dimensional testing in R. In Journal of Open Source Software.

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Nima Hejazi, Rachael Phillips, Alan Hubbard, Mark van der Laan (2018). methyvim: Targeted, robust, and model-free differential methylation analysis. In F1000Research.

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Jeremy Coyle, Nima Hejazi (2018). origami: A generalized framework for cross-validation in R. In Journal of Open Source Software.

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Nima Hejazi, Weixin Cai, Alan Hubbard (2017). biotmle: Targeted Learning for biomarker discovery. In Journal of Open Source Software.

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Nima Hejazi (2017). Generalized application of empirical Bayes statistics to asymptotically linear parameters. Master’s thesis, Graduate Division, University of California, Berkeley.

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Weixin Cai, Nima Hejazi, Alan Hubbard (2017). Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings.

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