Avi Baraz
PhD student, co-supervised with Prof. Uri Obolski.
Causal inference for antibiotic treatment and antimicrobial resistance.
Group
Current and former members of the Nevo research group.
People
Our group works on causal inference, networks, event-time outcomes, and statistical problems motivated by collaborations across disciplines.
Group snapshots
Current PhD students
Shahar Cohen
PhD student.
Causal inference under interference and high-dimensional interventions, with a focus on learning exposure mappings from data.
Bar Weinstein
PhD student.
Causal inference under network interference, with uncertain or partially observed networks.
Tamir Zehavi
PhD student.
Causal inference for event-time outcomes and truncation by death.
Current MSc students
Nir Aviv
MSc student.
Generalizing causal effects across populations with different characteristics and network structures, while accounting for spillover effects.
Tzlil Lijishal
MSc student.
Estimating causal effects under network interference when some treatment and spillover exposures are impossible, with a focus on positivity violations.
Alumni
PhD
Rachel Axelrod (PhD, 2026)
Thesis Topics in Causal Inference with Applications to Medical Data Related publication A sensitivity analysis approach for the causal hazard ratio in randomized and observational studies
Master’s and jointly supervised students
Noam Tetelman (MSc, 2026)
Current position Analyst, Riskified Thesis Identifying Influential Individuals in a Causal Network Through Random Effects Framework
Ron Tsibulsky (MSc in Computer Science, Tel Aviv University, 2025; joint supervision with Prof. Uri Shalit)
Current position Software Engineer, Google Thesis Set Valued Predictions For Robust Domain Generalization Related publication Set valued predictions for robust domain generalization
Itai Walk (MSc, 2024; joint supervision with Prof. Dan Zeltzer and Prof. Oren Danieli)
Current position Data Scientist, Google Thesis Falsification Tests of Instrumental Variables Using Negative Control Outcomes Related publication Negative control falsification tests for instrumental variable designs
Hadar Rosenwald (MSc, 2024, Technion IE&M; joint supervision with Prof. Uri Shalit)
Current position Head of Decisioning Company & AI/ML Guild, Sunbit Thesis Estimating Survivors’ Conditional Average Treatment Effect via Principal Stratification in Settings Where Outcomes May Be Truncated by ‘Death’
Karin Cohen (MSc, 2023)
Current position Research Team Lead, Pagaya Thesis Two one-sided tests approach for confounding bias detection using negative control exposures- Eyal Noy (MSc, 2023)
Thesis Causal Mediation Analysis in the Presence of Truncation by Death - Tzach Ben-Menachem (MPH, 2022, Hebrew University of Jerusalem; joint supervision with Dr. Raanan Raz)
Thesis Implementation of the Negative Control Exposure Method in Epidemiology – A Simulation Study 
Yael Naor (MSc, 2022; joint supervision with Dr. Itay Saporta-Eksten and Dr. Analia Schlosser)
Current position Data Scientist, Aidoc Thesis Estimating Heterogeneous Treatment Effects Using Causal Forests: Simulation, Application, and Policy Examination
Noam Barda (MSc, 2022)
Current position Associate Professor, Ben-Gurion University of the Negev Thesis Using Negative Controls to Adjust for Differential Misclassification with an Application to Studying the Impact of Booster Vaccines on Covid-19 Infections- Tamir Zehavi (MSc, 2021)
Current position PhD student Thesis A matching-based approach for identification and estimation of the survivor average causal effect Related publication Matching methods for truncation by death problems 
Amit Sasson (MSc, 2021)
Thesis Causal inference for heterogeneous disease etiology Related publication The subtype-free average causal effect for heterogeneous disease etiology- Avi Baraz (MSc, 2021; joint supervision with Prof. Uri Obolski)
Current position PhD student Thesis Revealing stable temporal relationships as a first step towards causal identification: an application to antibiotic resistance Related publication The time-varying association between previous antibiotic use and antibiotic resistance