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probably out getting more coffee
probably out getting more coffee

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@tlverse @CoVPN @nshlab @ictml-project

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nhejazi/README.md

I'm an academic (bio)statistician working at the interface of causal inference, debiased (or targeted) machine learning, semi-parametric statistics, and computational statistics.

  • I currently manage the NSH Lab (pronounced like "niche"), a (bio)statistical science research group focused on developing theory, methods, and open-source software for causal-analytic and statistical learning, most often for application to problems in the biomedical and public health sciences.
  • A while ago, I co-created and served as a core developer for the tlverse project, an open-source software ecosystem of R packages for Targeted Learning; the project includes an open-source handbook to guide implementation of the methods.

nima's github stats

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  1. tlverse/sl3 tlverse/sl3 Public

    💪 🤔 Modern Super Learning with Machine Learning Pipelines

    R 101 41

  2. haldensify haldensify Public

    📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

    R 17 5

  3. tlverse/hal9001 tlverse/hal9001 Public

    🤠 📿 The Highly Adaptive Lasso

    R 49 15

  4. tlverse/tmle3shift tlverse/tmle3shift Public

    🎯 🎲 Targeted Learning of the Causal Effects of Stochastic Interventions

    R 16 1

  5. txshift txshift Public

    📦 🎲 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling

    R 13 4

  6. Netflix/sherlock Netflix/sherlock Public

    R package for causal machine learning for segment discovery and analysis

    R 31 4