Nima Hejazi

Nima Hejazi

Assistant Professor of Biostatistics

Harvard T.H. Chan School of Public Health

Nima Hejazi's GitHub Activity


I am an Assistant Professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. Very recently, I was an NSF mathematical sciences postdoctoral research fellow, during which time I worked closely with Iván Díaz, David Benkeser, and Peter Gilbert. Prior to this, I obtained my PhD in biostatistics from UC Berkeley under the supervision of Mark van der Laan and Alan Hubbard. During that period, I was on the founding core development team of the tlverse project, an open source software ecosystem for targeted learning, and I enjoyed longstanding scientific collaborations with the Bill and Melinda Gates Foundation and the Fred Hutchinson Cancer Center.

My research interests combine causal inference and machine learning, aiming to develop robust, efficient, and assumption-lean statistical methods with a problem-first approach, tailored to issues arising in scientific collaborations. I am often, but not exclusively, motivated by methodological topics drawn from distribution-free (nonparametric) inference, semiparametric-efficient inference, high-dimensional inference, targeted minimum loss estimation, and modern issues in the design of experiments (outcome-dependent sampling, sequentially adaptive treatments), for applications in clinical trials and computational biology. Although my substantive scientific interests are diverse, I have recently been captivated by statistical issues stemming from vaccine efficacy trials and infectious disease epidemiology. I am also deeply interested in high-performance statistical computing and open source software design to promote reproducibility in applied statistics and data science.


  • Causal Inference, Missing Data, and Causal Machine Learning
  • Nonparametric Inference and Statistical Machine Learning
  • Modern Study Designs and Adaptive Experimentation
  • High-Dimensional Inference and Computational Biology
  • Statistical Computing and Reproducible Data Science


  • 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

Recent & Upcoming Talks

Efficient Estimation of Modified Treatment Policy Effects Based on the Generalized Propensity Score
A Framework for Causal Segmentation Analysis with Machine Learning in Large-Scale Digital Experiments
Nonparametric Estimation of the Generalized Propensity Score Based on the Highly Adaptive Lasso
Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials
Leveraging the Causal Effects of Stochastic Interventions to Evaluate Vaccine Efficacy in Two-phase Trials


current courses

I won’t be teaching during the 2022-2023 academic year. I’ll resume in 2023-2024.

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.


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