Ehud Karavani


May 4, 2024

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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.


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)
  • Individual Contributor (IC)
    Causal inference consultant for Research labs globally

  • Project leader: drug repurposing by applying high-throughput causal inference to observational healthcare data

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

    • Generating 100s of hypotheses in minutes

    • Served several external engagements with pharma clients, bringing millions in revenue

  • Organized the 2018 Atlantic Causal Inference Conference Data Challenge

  • Mentored interns and students.
    Onboarding lead

  • Published papers and issued patents


Applied Statistician
Laboratory for Gait & Neurodynamics, Ichilov Hospital

  • Bayesian hierarchical/multilevel models and causal inference for gait analysis in multiple sclerosis patients
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, Hebrew University, Faculty of Medicine

  • Large-scale RNA analysis for finding high-resolution protein-RNA interactions


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

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)

Bachelor’s thesis published in Nucleic Acids Research


Programming skills
  • Python scientific stack (fluent)

    • Scikit-Learn, Pandas, Statsmodels, Seaborn (objects), Matplotlib, Altair, Streamlit, Pytorch, Keras, cvxpy, PyMC, Bambi, Arviz…
  • R (when needed)

  • Git + GitHub

  • Continuous integration (Travis, GitHub Actions)

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

  • Fluent English

  • Native Hebrew

  • Data enthusiast: past DataHack mentor and judge

  • Musician 🎸, hiker / backpacker 🏔️ | | |

  • Friendly 🙂



IBM-Research Accomplishment

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


Best of RSNA

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


Best Talk: Israeli Population Genetics Meeting

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


Featured Theory of the issue (Cell)

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


Date Title Venue DOI
2024 Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores Epidemiology
2023 Causalvis: Visualizations for Causal Inference CHI: Conference on Human Factors in Computing Systems
2023 FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization Pacific Symposium on Biocomputing
2021 Trends in clinical characteristics and associations of severe non-respiratory events related to SARS-CoV-2 MedRxiv
2019 Screening human embryos for polygenic traits has limited utility Cell
2019 A discriminative approach for finding and characterizing positivity violations using decision trees Arxiv
2019 Predicting breast cancer by applying deep learning to linked health records and mammograms Radiology
2019 An evaluation toolkit to guide model selection and cohort definition in causal inference Arxiv
2019 Comment: causal inference competitions: where should we aim? Statistical Science
2018 In vivo cleavage rules and target repertoire of RNase III in Escherichia coli Nucleic Acids Research
2018 Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis Arxiv
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May go out of date. Please see my Google Scholar page for the most up-to-date information.