Nima Hejazi

Nima Hejazi

NSF Postdoctoral Research Fellow in Biostatistics

Weill Cornell Medicine

Nima Hejazi's GitHub Activity

Biography

I am an NSF postdoctoral research fellow in biostatistics at Weill Cornell Medicine, working with Iván Díaz and collaborating with David Benkeser and Peter Gilbert. In 2021, I completed my PhD in biostatistics under the joint supervision of Mark van der Laan and Alan Hubbard at UC Berkeley. During my graduate years, I served on the founding core development team for the tlverse project, a modern software ecosystem for targeted learning, and enjoyed long-term collaborations with the Bill & Melinda Gates Foundation, Fred Hutchinson Cancer Research Center, and Netflix. In July, I will begin a faculty appointment as a (tenure-track) assistant professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health.

My research interests sit at the intersection of nonparametric causal inference and machine learning, driven by the central aim of developing novel, assumption-lean statistical procedures tailored for the efficient estimation of and robust inference on scientifically informative quantities. I am motivated largely by methodological issues arising from high-dimensional inference, targeted loss-based estimation, and non/semi-parametric theory, usually inspired by applications in clinical trials and computational biology. Though my substantive scientific interests are diverse, I have been especially interested in statistical problems stemming from infectious disease epidemiology, immunology, and vaccine efficacy trials. I am also deeply interested in high-performance statistical/numerical computing, research software engineering, and open source software for reproducible statistical data science.

Interests

  • Causal Inference and Censored Data Models
  • Nonparametric Estimation and Machine Learning
  • Semiparametric Theory and Robust Statistics
  • Statistical Computing and Statistical Data Science
  • 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
February 2022 In Biostatistics

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 PDF Code Project Project DOI
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 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)

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Nima Hejazi, David Benkeser, Iván Díaz, Mark van der Laan (2022). Efficient estimation of modified treatment policy effects based on the generalized propensity score.

Preprint Project Project Project Project Slides

Ashkan Ertefaie, Nima Hejazi, Mark van der Laan (2022). Nonparametric inverse probability weighted estimators based on the highly adaptive lasso. In Biometrics.

Preprint Project Project Slides

Iván Díaz, Katherine Hoffman, Nima Hejazi (2022). Causal survival analysis under competing risks using longitudinal modified treatment policies.

Preprint Project Project

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

Preprint PDF Code Project Project DOI

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

PDF Code Project Project Source Document 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

I will not be teaching during the 2022-2023 academic year, but I plan to resume teaching in the 2023-2024 academic year.

past courses

upcoming workshops

recent workshops

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

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

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] | [Paper]

  • 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.

Other Assorted Adventures

Projects

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All Causal Inference Biostatistics Targeted Learning Personal
Causal Machine Learning
Causal Subgroup Discovery
Nonparametric Sieve Estimation
Causal Vaccine Efficacy Evaluation
The Highly Adaptive Lasso Estimator
(Contrastive) Covariance Estimation
Nonparametric Causal Mediation Analysis
Causal Effects of Stochastic Interventions
Semiparametric Variance Moderation
Distractions
The PhD Years

Contact