Ehud Karavani

Modified

October 9, 2025

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Highlights

  • Healthcare machine learning researcher with 8+ years of experience

  • EB1-A visa approved (US)

  • 1st author Cell paper, 2019

  • Creator of causallib - an open-source Python package for causal inference.
    750+ stars and 100+ forks on Github.

  • Received an IBM Research Accomplishment award (2023)

  • Co-inventor on 3 US patents

  • PyData conference speaker, lecturer, and podcast interviewee

  • Causal inference, machine learning, deep learning, statistics, data viz, Python

About me

Highly skilled in causal inference, machine learning, (Bayesian) statistics, and data visualization. An applied researcher and data scientist, I spend my time between building reusable tools for research and putting them into use. Advocating Clean Code for research code. Strong preference for eclectic, collaborative environments.

Experience

2017 – present

Research Staff Member
Causal Machine Learning for Healthcare and Life Science, IBM-Research, Israel

  • Creator of causallib – a one-stop-shop open-source Python package for flexible causal inference modeling.

    • Received an IBM Research Accomplishment award (2023)

  • Client project leader from start to finish: translating vague clinicians’ questions into concrete statistical hypotheses, answering them, and communicating the findings

  • Led and designed a drug repurposing software asset, applying high-throughput causal inference to observational healthcare data

    • Managing a team of 5 researchers.
      Leading the scientific pipeline, system design, and visualization app

    • Generating 100s of hypotheses in minutes

    • Serving 4 external engagements with top pharma clients, bringing millions in revenue

  • Individual Contributor (IC)
    Causal inference consultant for projects in the US, UK, France, Japan, Kenya, South Africa, and Switzerland

  • Led global strategy at IBM Research for causality in drug discovery

    • Oversaw research of adaptive experimentation using Bayesian inference

  • Mentored 10+ students and interns
    Onboarding lead, onboarding 10+ researchers

  • Published 10+ papers and issued 3 US patents

2024:

  • Deconfounding transformer-based large language models for biological sequence, overcoming batch effects across and within diverse data sources

  • GLM-ification of deep learning models, bringing established biostatistics concepts into transformer-based deep learning models

2025:

  • Quantum Advantage task force member [2025]: developing and testing quantum algorithms for combinatorial optimization problems
2022

Principle Statistician
Laboratory for Gait & Neurodynamics, Ichilov Hospital

  • Bayesian hierarchical/multilevel models and causal inference for gait analysis in multiple sclerosis patients
    • Bayesian multilevel models for hurdle models of repeated patients’ measurements
    • Formal causal inference with DAGs for minimizing inessential tests, saving over 3 hours of unnecessary tests by clinicians per patient.
2016 – 2017

Teaching Assitant
The School of Computer Science, Hebrew University

  • Introduction to Data Science

  • Workshop in Computational Bioskills
2015 – 2016

Research Associate / Computational Biologist
Institue for Medical Research Israel-Canada, Hadassah Hospital

  • Developing novel methodologies for finding high-resolution protein-RNA interactions using high-volume data

Education

2016 – 2019

M.Sc. in Computer Science and Computational Biology
Faculty of Science, the Hebrew University of Jerusalem, Israel

Thesis: quantifying the utility of embryo selection using genomic prediction of traits
published in Cell

  • Predicting physical traits from DNA (GWAS) using classical, machine learning, and deep learning methods

  • Pioneering the effects of prediction-based embryo selection in IVF
2013 – 2016

B.Sc. in Computer Science and Computational Biology
Faculty of Science, the Hebrew University of Jerusalem, Israel

  • Dean’s List of Academic Excellence (2016)
  • Research scholarship from IMRIC (2016)

Bachelor’s thesis published in Nucleic Acids Research

Community

Skills

Programming skills

  • Python scientific stack (fluent)

    • Pandas, Numpy, Scikit-Learn, Statsmodels, PyTorch (lightning), Bambi, PyMC, Arviz, Matplotlib, Seaborn (objects). Altair, Streamlit, cvxpy, Pydantic, Hydra,

  • R (when needed)

  • Git + GitHub

  • Continuous development (Travis, GitHub Actions)

  • Linux and remote development (Cloud/AWS + Jupyter lab / VS Code)
Languages

  • Fluent English

  • Native Hebrew
General

  • Data enthusiast

  • Musician 🎸, hiker / backpacker 🏔️ | |

  • Friendly 🙂

Awards

2023

IBM-Research Accomplishment

For my work on causallib and research engagement with the Cleveland Clinic Foundation
2019

Best of RSNA

For the paper Predicting Breast Cancer by Applying Deep Learning to Linked Health Records and Mammograms, published in Radiology
2019

Best Talk: Israeli Population Genetics Meeting

For the paper Screening Human Embryos for Polygenic Traits has Limited Utility
2019

Featured Theory of the issue (Cell)

For the paper Screening Human Embryos for Polygenic Traits has Limited Utility
2016 Dean’s list of academic excellence

Publications

Date Title Venue DOI
2025 Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation AISTATS https://proceedings.mlr.press/v258/ter-minassian25a.html
2025 Peri-operative anti-inflammatory drug use and seizure recurrence after resective epilepsy surgery: Target trials emulation iScience https://doi.org/10.1016/j.isci.2025.112124
2024 Single-microglia transcriptomic transition network-based prediction and real-world patient data validation identifies ketorolac as a repurposable drug for Alzheimer’s disease Alzheimer’s & Dementia https://doi.org/10.1002/alz.14373
2024 Using Causal Inference to Investigate Contraceptive Discontinuation in Sub-Saharan Africa International Joint Conference on Artificial Intelligence (IJCAI) https://doi.org/10.24963/ijcai.2024/792
2024 Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores Epidemiology https://doi.org/10.1097/ede.0000000000001733
2023 Causalvis: Visualizations for Causal Inference CHI: Conference on Human Factors in Computing Systems https://doi.org/10.1145/3544548.3581236
2023 FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization Pacific Symposium on Biocomputing https://doi.org/10.1142/9789811270611_0019
2021 Trends in clinical characteristics and associations of severe non-respiratory events related to SARS-CoV-2 MedRxiv https://doi.org/10.1101/2021.03.24.21251900
2019 Screening human embryos for polygenic traits has limited utility Cell https://doi.org/10.1016/j.cell.2019.10.033
2019 A discriminative approach for finding and characterizing positivity violations using decision trees Arxiv https://doi.org/10.48550/arXiv.1907.08127
2019 Predicting breast cancer by applying deep learning to linked health records and mammograms Radiology https://doi.org/10.1148/radiol.2019182622
2019 An evaluation toolkit to guide model selection and cohort definition in causal inference Arxiv https://doi.org/10.48550/arXiv.1906.00442
2019 Comment: causal inference competitions: where should we aim? Statistical Science https://doi.org/10.1214/18-STS679
2018 In vivo cleavage rules and target repertoire of RNase III in Escherichia coli Nucleic Acids Research https://doi.org/10.1093/nar/gky684
2018 Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis Arxiv https://doi.org/10.48550/arXiv.1802.05046
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May go out of date. Please see my Google Scholar page for the most up-to-date information.