Nima Hejazi

NSF Postdoctoral Research Fellow in Biostatistics

Weill Cornell Medicine

Biography

I am an NSF postdoctoral research fellow in biostatistics at Weill Cornell Medicine, working with Iván Díaz and collaborating with David Benkeser. I just completed my PhD in biostatistics at UC Berkeley, under the guidance of Mark van der Laan and Alan Hubbard. During my graduate studies, I served as a founding core developer of the tlverse project, the software ecosystem for targeted learning, and enjoyed collaborations with the Bill & Melinda Gates Foundation, Fred Hutchinson Cancer Research Center, Kaiser Permanente Division of Research, Pandora, and Netflix.

My research interests sit at the intersection of nonparametric causal inference and machine learning, particularly in the development of statistical procedures tailored for efficient estimation and robust inference in flexible statistical models. Broadly, I am motivated by methodological issues arising from high-dimensional inference, targeted loss-based estimation, and non/semi-parametric theory, usually inspired by applications in vaccine clinical trials, epidemiology, and computational biology. I am also interested in high-performance statistical/numerical computing, research software engineering, and open source software for reproducible data science.

Interests

Causal Inference and Censored Data Models
Nonparametric Estimation and Machine Learning
Semiparametric Theory and Robust Statistics
Statistical Computing and Research Software Engineering
High-Dimensional and Computational Biology

Education

PhD in Biostatistics (designated emphasis in Computational & Genomic Biology), 2021

University of California, Berkeley
MA in Biostatistics, 2017

University of California, Berkeley
BA with a triple major in Molecular & Cell Biology (em. Neurobiology), Psychology, and Public Health, 2015

University of California, Berkeley

Featured Publications

Nima Hejazi, Kara Rudolph, Mark van der Laan, Iván Díaz

September 2020

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects

Causal mediation analysis has historically been limited in two important regards: (i) a focus has traditionally been placed on binary treatments and static interventions, and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by treatment. We present a theoretical study of an (in)direct effect decom- position of the population intervention effect, defined by stochastic interventions jointly applied to the treatment and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether a treatment is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by treatment. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, all the while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open source software implementing the proposed methodology.

Preprint Project Project

Nima Hejazi, Mark van der Laan, Holly Janes, Peter Gilbert, David Benkeser

September 2020 In Biometrics

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. Despite this success, effective vaccines to prevent many high-burden diseases, including HIV, have been slow to develop. Vaccine development can be aided by the identification of immune response markers that serve as effective surrogates for clinically significant infection or disease endpoints. However, measuring immune response is often costly, which has motivated the usage of two-phase sampling for immune response sampling in clinical trials of preventive vaccines. In such trials, measurement of immunological markers is performed on a subset of trial participants, where enrollment in this second phase is potentially contingent on the observed study outcome and other participant-level information. We propose nonparametric methodology for efficiently estimating a counterfactual parameter that quantifies the impact of a given immune response marker on the subsequent probability of infection. Along the way, we fill in a theoretical gap pertaining to the asymptotic behavior of nonparametric efficient estimators in the context of two-phase sampling, including a multiple robustness property enjoyed by our estimators. Techniques for constructing confidence intervals and hypothesis tests are presented, and an open source software implementation of the methodology, the txshift R package, is introduced. We illustrate the proposed techniques using data from a recent preventive HIV vaccine efficacy trial.

Preprint PDF Code Project Project Slides DOI

Iván Díaz, Nima Hejazi

February 2020 In Journal of the Royal Statistical Society, Series B (Statistical Methodology)

Causal mediation analysis for stochastic interventions

Mediation analysis in causal inference has traditionally focused on binary treatment regimes and deterministic interventions, as well as a decomposition of the average treatment effect in terms of direct and indirect effects. In this paper we present an analogous decomposition of the population intervention effect, defined through stochastic interventions. Population intervention effects provide a generalized framework in which a variety of interesting causal contrasts can be defined, including effects for continuous and categorical exposures. We show that identification of direct and indirect effects for the population intervention effect requires weaker assumptions than its average treatment effect counterpart. In particular, identification of direct effects is guaranteed in experiments that randomize the treatment and the mediator. We discuss various estimators of the direct and indirect effects, including substitution, re-weighted, and efficient estimators based on flexible regression techniques. Our efficient estimator is asymptotically linear under a condition requiring $n^{\frac{1}{4}}$-consistency of certain regression functions. We perform a simulation study in which we assess the finite-sample properties of our proposed estimators. We present the results of an illustrative study where we assess the effect of participation in a sports team on BMI among children, using mediators such as exercise habits, daily consumption of snacks, and overweight status.

Preprint PDF Code Project Project DOI

Recent Publications

(see CV for a full list)

Quickly discover relevant content by filtering publications.

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

Preprint Code Project Project Slides

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

PDF Code Project Project Project Project

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.

PDF Code Source Document DOI

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

Preprint Code

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

Preprint PDF Code Project DOI

See all publications

Recent & Upcoming Talks

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

Wed, Nov 17, 2021 10:30 AM New York, New York, United States (remote due to COVID-19)

A Framework for Causal Segmentation Analysis with Machine Learning in Large-Scale Digital Experiments

Fri, Nov 5, 2021 2:30 PM Boston, Massachusetts, United States (remote due to COVID-19)

Nonparametric Estimation of the Generalized Propensity Score Based on the Highly Adaptive Lasso

Wed, May 19, 2021 6:30 AM Oslo, Norway (remote due to COVID-19)

Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials

Wed, Jan 20, 2021 12:00 PM Seattle, Washington, United States (remote due to COVID-19)

Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials

Wed, Dec 16, 2020 11:45 AM Boston, Massachusetts, United States (remote due to COVID-19)

See all talks

Teaching

current courses

Nothing on tap for the 2021-2022 academic year. Maybe next year…

past courses

Public Health 290: Biomedical Big Data Capstone Seminar, as graduate student instructor with Prof. Mark van der Laan; Spring 2021 at UC Berkeley. Introduced as part of the Biomedical Big Data Training Program; now offered regularly as Public Health 243B: Targeted Learning in Practice.
Public Health C240B: Survival Analysis and Causality (also Statistics C245B), as graduate student instructor with Prof. Mark van der Laan; Fall 2020 at UC Berkeley.
Public Health 290: Biomedical Big Data Capstone Seminar, as graduate student instructor with Prof. Alan Hubbard; Spring 2020 at UC Berkeley. Introduced as part of the Biomedical Big Data Training Program.
Public Health C242C: Longitudinal Data Analysis (also Statistics C247C), as graduate student instructor with Prof. Alan Hubbard; Fall 2019 at UC Berkeley.
Public Health 290: Targeted Learning in Biomedical Big Data, as graduate student instructor with Prof. Mark van der Laan; Spring 2018 at UC Berkeley. Introduced as part of the Biomedical Big Data Training Program; now offered regularly as Public Health 243A: Targeted Learning.

upcoming workshops

Modern Causal Mediation Analysis at the Society for Epidemiologic Research meeting; 2022 May (online) and 2022 June (in-person); to be co-taught with Iván Díaz and Kara Rudolph.
Targeted Learning I: Causal Inference Meets Ensemble Machine Learning at the Society for Epidemiologic Research meeting; 2022 June; to be co-taught with Mark van der Laan, Alan Hubbard, Jeremy Coyle, Ivana Malenica, and Rachael Phillips.
Targeted Learning II: Advanced Applications of Causal Inference at the Society for Epidemiologic Research meeting; 2022 June; to be co-taught with Mark van der Laan, Alan Hubbard, Jeremy Coyle, Ivana Malenica, and Rachael Phillips.

recent workshops

Targeted Learning in the tlverse: Causal Inference Meets Ensemble Machine Learning at the Society for Epidemiologic Research Meeting; 2021 June; co-taught with Mark van der Laan, Alan Hubbard, Jeremy Coyle, Ivana Malenica, and Rachael Phillips.
Modern Causal Mediation Analysis at the Society for Epidemiologic Research Meeting; 2021 May; co-taught with Iván Díaz and Kara Rudolph.
Targeted Learning in the tlverse: Causal Inference Meets Ensemble Machine Learning at the Eastern North American Region of the International Biometric Society (ENAR) Spring Meeting; 2021 March; co-taught with Mark van der Laan, Alan Hubbard, Jeremy Coyle, Ivana Malenica, and Rachael Phillips.
The tlverse Software Ecosystem for Targeted Learning at the Conference on Statistical Practice; 2020 February; co-taught with Alan Hubbard, Jeremy Coyle, Ivana Malenica, and Rachael Phillips.
The tlverse Software Ecosystem for Causal Inference at the Atlantic Causal Inference Conference; 2019 May; co-taught with Mark van der Laan, Alan Hubbard, Jeremy Coyle, Ivana Malenica, and Rachael Phillips.

Carpentries workshops

I am a member of Software Carpentry and Data Carpentry, through which I work on curriculum development, maintenance of lesson materials, and workshop delivery.

Software Carpentry: Shell, Git, and R at the Berkeley Institute for Data Science; 2019 January; co-taught with Scott Peterson and Nelle Varoquaux.
Course materials here | GitHub repository here
Software Carpentry: Shell, Git, and Python at the Berkeley Institute for Data Science; 2018 July; co-taught with Kunal Marwaha.
Course materials here | GitHub repository here
Data Carpentry: Genomics at Lawrence Berkeley National Laboratory; 2018 May; co-taught with Adam Orr.
Course materials here | GitHub repository here

Software

Collected collateral damage from doing statistics research, hopefully useful to others.

Targeted Learning in the `tlverse`

The tlverse is an ecosystem of R packages for Targeted Learning, of which I am a co-founder and core developer. A few of the tlverse packages to which I’ve made significant contributions include

sl3: An R package providing a modern implementation of the Super Learner ensemble modeling algorithm that simultaneously exposes a flexible grammar for composing arbitrary pipelines for machine learning tasks. Joint work with Jeremy Coyle, Ivana Malenica, Rachael Phillips, and Oleg Sofrygin.
[Docs] | [GitHub]
origami: An R package exposing a generalized framework for applying a great variety of cross-validation schemes to arbitrary estimation functions. Joint work with Jeremy Coyle, Ivana Malenica, and Rachael Phillips.
[Docs] | [GitHub] | [CRAN] | [Paper]
hal9001: An R package providing an efficient implementation of the Highly Adaptive Lasso (HAL), a nonparametric regression estimator achieving near-parametric convergence rates under relatively mild assumptions. Joint work with Jeremy Coyle, Rachael Phillips, Lars van der Laan and Mark van der Laan.
[Docs] | [GitHub] | [CRAN] | [Paper]
tmle3shift: An R package for targeted maximum likelihood estimation of the causal effects of modified treatment policies on continuous-valued exposures, incorporates working marginal structural models for summarization of effect estimates. Joint work with Jeremy Coyle and Mark van der Laan.
[Docs] | [GitHub]

Causal Machine Learning

A significant focus of my research program centers on the intersection of causal inference and statistical machine learning. I’ve (co-)developed R packages for a range of problems: causal mediation analysis, evaluating the effects of stochastic interventions under two-phase sampling, conditional density estimation, causal segment discovery and offline policy evaluation, and survival analysis.

sherlock: An R package for employing causal machine learning and non/semi-parametric estimation to discover population segments (or subgroups) based on treatment effect heterogeneity. Flexible techniques for defining segment-specific treatment rules and efficient estimators of the causal effects of these dynamic treatment regimes are implemented. Joint work with Wenjing Zheng as part of an internship at Netflix Research.
[Docs] | [GitHub]
medshift: An R package for estimating the population intervention (in)direct effects based on stochastic interventions. Classical and efficient estimators are supported for the effects of incremental propensity score interventions and modified treatment policies. Joint work with Iván Díaz.
[Docs] | [GitHub]
medoutcon: An R package for efficient estimation of interventional (in)direct effects subject to intermediate confounding, including one-step and targeted minimum loss estimators. Joint work with Iván Díaz and Kara Rudolph.
[Docs] | [GitHub]
txshift: An R package for efficient estimation of and inference on causal effects of stochastic interventions on continuous-valued exposures. Robust estimation and efficient inference under two-phased sampling is supported. Joint work with David Benkeser.
[Docs] | [GitHub] | [CRAN] | [Paper]
haldensify: An R package for nonparametric conditional density estimation based on the highly adaptive lasso, designed for estimating the generalized propensity score. Joint work with David Benkeser and Mark van der Laan.
[Docs] | [GitHub] | [CRAN]
survtmle: An R package for the construction of targeted maximum likelihood estimates of marginal cumulative incidence in right-censored survival settings with and without competing risks, including estimation procedures that respect bounds. Joint work with David Benkeser.
[Docs] | [GitHub] | [CRAN]

High-Dimensional Biology

A parallel thread of my research concerns the development of novel statistical methodology for application in high-dimensional and computational biology. I have (co-)developed several R packages extending the Bioconductor Project.

biotmle: An R package for the model-free discovery of biomarkers from biological expression data, introducing a generalization of moderated statistics for variance stabilization of semiparametric estimators. Joint work with Alan Hubbard and Mark van der Laan.
[Docs] | [GitHub] | [Bioconductor] | [Paper]
scPCA: An R package for sparse contrastive principal component analysis, facilitating the recovery of stable and low-dimensional patterns from high-dimensional biological data while removing technical artifacts by making use of control samples. Joint work with Philippe Boileau and Sandrine Dudoit.
[GitHub] | [Bioconductor] | [Paper]
methyvim: An R package for genome-wide analysis of differential methylation across CpG sites, applying causal variable importance. Joint work with Mark van der Laan. Deprecated after Bioconductor v3.12.
[Docs] | [GitHub] | [Bioconductor]
adaptest: An R package for multiple hypothesis testing with data adaptive target parameters in high-dimensional settings using Targeted Learning. Joint work with Weixin Cai and Alan Hubbard. Deprecated after Bioconductor v3.12.
[GitHub] | [Bioconductor] | [Paper]

Other Assorted Adventures

cvCovEst: An R package for asymptotically optimal, cross-validated, loss-based selection of covariance matrix estimators, tailored for use in high-dimensional settings. Joint work with Philippe Boileau, Brian Collica, Mark van der Laan, and Sandrine Dudoit.
[Docs] | [GitHub] | [CRAN] | [Paper]
nima: An R package housing my personal R toolbox, written to support statistical computing for research.
[Docs] | [GitHub] | [CRAN]