Noam Barda, MD, PhD

Causal inference, prediction and fairness for real-world health data

I'm a physician, epidemiologist and computer scientist. I develop and apply methods for learning from the data health systems routinely collect (electronic health records, registries and claims) to answer two questions:

Does an intervention work, and is it safe?
Who should receive it, and are the models that guide the decision fair?

Most of my applied work is on infectious diseases and vaccines. Some of it has informed decisions at Clalit Health Services and Israel's Ministry of Health.

Associate Professor, Ben-Gurion University of the Negev
Institute for Interdisciplinary Computational Science · Department of Epidemiology, Biostatistics and Community Health Sciences
Chief Epidemiologist, Sheba Pandemic Research Institute (SPRI)
Sheba Medical Center

noam@noambarda.com CV (PDF) Google Scholar (external site) ORCID (external site) BGU profile (external site)

Research

Causal inference

I estimate the effectiveness and safety of vaccines, drugs and other interventions using routinely collected health data. My approach centers on target trial emulation: designing observational studies around the randomized trial we would ideally conduct.

Prediction for decisions

I develop and validate prediction models to inform clinical and public health decisions. My work includes a COVID-19 mortality risk model developed without individual-level data and used by Clalit Health Services and Israel’s Ministry of Health.

Fairness

I develop methods to assess and improve fairness in clinical prediction models. My focus is calibration across patient groups—whether predicted risks match observed outcomes—including when models are applied to populations different from those used for development.

Selected publications

A full list is on Google Scholar (external site) and ORCID (external site). An asterisk marks equal contribution.

Causal inference

West Nile virus–neutralizing plasma for West Nile virus disease (external site)

NEJM Evidence · 2026

Regev-Yochay G, Barda N, Shusterman Y, et al., Yahav D.
Antibiotic use and the risk of hospital-onset Clostridioides difficile infection (external site)

JAMA Network Open · 2025

Gilboa M, Regev-Yochay G, Meltzer E, Cohen I, Peretz Y, Zilberman-Daniels Y, Segev A, Amit S, Yahav D, Barda N.
Indirect protection of children from SARS-CoV-2 infection through parental vaccination (external site)

Science · 2022

Hayek S, Shaham G, Ben-Shlomo Y, Kepten E, Dagan N, Nevo D, Lipsitch M, Reis BY, Balicer RD, Barda N.
BNT162b2 vaccine effectiveness against Omicron in children 5 to 11 years of age (external site)

New England Journal of Medicine · 2022

Cohen-Stavi CJ, Magen O, Barda N, Yaron S, Peretz A, Netzer D, Giaquinto C, Judd A, Leibovici L, Hernán MA, Lipsitch M, Reis BY, Balicer RD, Dagan N.
Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study (external site)

The Lancet · 2021

Barda N*, Dagan N*, Cohen C, Hernán MA, Lipsitch M, Kohane IS, Reis BY, Balicer RD.
Myocarditis after Covid-19 vaccination in a large health care organization (external site)

New England Journal of Medicine · 2021

Witberg G*, Barda N*, Hoss S, Richter I, Wiessman M, Aviv Y, Grinberg T, Auster O, Dagan N, Balicer RD, Kornowski R.
Effectiveness of the BNT162b2 mRNA COVID-19 vaccine in pregnancy (external site)

Nature Medicine · 2021

Dagan N*, Barda N*, Biron-Shental T, Makov-Assif M, Key C, Kohane IS, Hernán MA, Lipsitch M, Hernandez-Diaz S, Reis BY, Balicer RD.
Safety of the BNT162b2 mRNA Covid-19 vaccine in a nationwide setting (external site)

New England Journal of Medicine · 2021

Barda N*, Dagan N*, Ben-Shlomo Y, Kepten E, Waxman J, Ohana R, Hernán MA, Lipsitch M, Kohane I, Netzer D, Reis BY, Balicer RD.
BNT162b2 mRNA Covid-19 vaccine in a nationwide mass vaccination setting (external site)

New England Journal of Medicine · 2021

Dagan N*, Barda N*, Kepten E, Miron O, Perchik S, Katz MA, Hernán MA, Lipsitch M, Reis B, Balicer RD.

Prediction

Predicting chronic subdural hematoma risk in elderly patients with mild traumatic brain injury (external site)

Acta Neurochirurgica · 2026

Ofri M, Azriel A, Kahati L, Paran N, Esbit S, Sufaro Y, Barda N.
Predicting antibody kinetics and duration of protection against SARS-CoV-2 following vaccination from sparse serological data (external site)

PLOS Computational Biology · 2025

Deichmann J, Barda N, Canetti M, Peretz Y, Weiss-Ottolenghi Y, Lustig Y, Regev-Yochay G, Lipsitch M.
Developing a COVID-19 mortality risk prediction model when individual-level data are not available (external site)

Nature Communications · 2020

Barda N, Riesel D, Akriv A, Levy J, Finkel U, Yona G, Greenfeld D, Sheiba S, Somer J, Bachmat E, Rothblum GN, Shalit U, Netzer D, Balicer RD, Dagan N.
Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization (external site)

Nature Medicine · 2020

Dagan N, Elnekave E, Barda N, Bergman-Amitai O, Bar A, Orlovsky M, Bachmat E, Balicer RD.
Prediction of acute myeloid leukaemia risk in healthy individuals (external site)

Nature · 2018

Abelson S, Collord G, Ng SW, Weissbrod O, Cohen NM, Niemeyer E, Barda N, et al., Shlush LI.

Fairness

A post-processing fairness mitigation method for medical prediction models (external site)

IEEE Journal of Biomedical and Health Informatics · 2026

Stern N, Livni-Navon I, Reingold O, Bachmat E, Barda N*, Dagan N*.
Multiaccuracy for subpopulation calibration over distribution shift in medical prediction models (external site)

Conference on Health, Inference, and Learning (PMLR 287) · 2025

Kapash D, Balicer RD, Reingold O, Dagan N, Barda N.
Bridging the gap: towards an expanded toolkit for AI-driven decision-making in the public sector (external site)

Government Information Quarterly · 2024

Fischer-Abaigar U, Kern C, Barda N, Kreuter F.
Addressing bias in prediction models by improving subpopulation calibration (external site)

Journal of the American Medical Informatics Association · 2020

Barda N, Yona G, Rothblum GN, Greenland P, Leibowitz M, Balicer RD, Bachmat E, Dagan N.

Teaching

I teach physicians and computer scientists in the same classrooms, and I build each course from first principles: practice in health data science should rest on a real understanding of the epidemiology, statistics and computer science beneath it. I developed all of these courses, alone or jointly.

For a sense of my teaching style, watch Opportunities for the Application of Quantitative Models in a Fully Integrated Healthcare System (external site). I gave this lecture with Noa Dagan as part of the Theoretically Speaking series at the Simons Institute for the Theory of Computing.

In 2025 I received the Excellence in Teaching Award of the Faculty of Computer and Information Science at Ben-Gurion University.

Medical Prediction ModelsMaster's elective
Introduction to Biomedical InformaticsMaster's core course
Advanced Topics in EpidemiologyMaster's elective
Introduction to Epidemiology for Medical StudentsUndergraduate core course
Effective Writing of Scientific PapersMaster's workshop
Health AI for ExecutivesExecutive course

Short biography

Noam Barda, MD, PhD, is an Associate Professor at Ben-Gurion University of the Negev, appointed jointly to the Institute for Interdisciplinary Computational Science and the Department of Epidemiology, Biostatistics and Community Health Sciences. He is also Chief Epidemiologist of the Sheba Pandemic Research Institute.

His research develops and applies methods for causal inference, prediction and fairness using the data that health systems routinely collect. Most of his applied work is on infectious diseases and vaccines.

He trained as a physician at Tel Aviv University (MD), specialized in public health, and holds a BSc in computer science, an MSc in biostatistics, an MBA and a PhD from Ben-Gurion University on cardiovascular risk prediction. He was a postdoctoral fellow in the Department of Biomedical Informatics at Harvard Medical School with Isaac Kohane and Ben Reis. Before joining BGU he was Head of Epidemiology and Research at the Clalit Research Institute.

He serves on Israeli Ministry of Health committees on clinical trials of digital medical devices and on AI-based health research, and on the managing committee of the Israel Association of Public Health Physicians.

Contact

I supervise MSc and PhD students in computer science and epidemiology, and I'm happy to hear from prospective students.

I am also glad to hear from prospective collaborators and anyone working on similar problems in academia or industry.

noam@noambarda.com

Ben-Gurion University of the Negev
Ben-Gurion Blvd 1, Be'er Sheva, Israel

Google Scholar (external site) ORCID 0000-0002-3400-235X (external site) BGU research portal (external site)