Negative controls · Causal inference · Public health & medicineMethods & theory
When negative control exposures fail to detect bias
With Raanan Raz. Accepted for publication in the American Journal of Epidemiology.
Causal inference · survival analysis · networks
Daniel Nevo
I am an Associate Professor in the Department of Statistics and Operations Research at Tel Aviv University.
I develop statistical methods for causal inference in complex settings. My research combines methodological work with collaborations in medicine, epidemiology, economics, and machine learning, including the use of causal methods to understand how language models make predictions.
Biography
Short bio
Available for seminar introductions, conference programs, and professional profiles.
Daniel Nevo is an Associate Professor in the Department of Statistics and Operations Research at Tel Aviv University. He develops statistical methods for causal inference and survival analysis, with particular interests in network interference, event-time outcomes, causal diagnostics, and generalizability. His work is motivated by collaborations in medicine, epidemiology, public health, economics, and computer science. He received his PhD in Statistics from the Hebrew University of Jerusalem and completed postdoctoral training in Biostatistics and Epidemiology at the Harvard T.H. Chan School of Public Health.
Updates
Recent papers and news
Cancer epidemiology · Public health & medicineApplied research
Three Decades of Pancreatic Cystic Lesions: Diagnostic and Management Trends in a Prospective Single-Center Cohort of Over 5,000 Patients
Accepted for publication in the American Journal of Gastroenterology.
Back from sabbatical at MIT and Harvard
I’m back at Tel Aviv University after my 2025–2026 sabbatical as an MIT-Kalaniyot Sabbatical Scholar at MIT’s Institute for Data, Systems, and Society (IDSS), and a Faculty Visitor in Harvard University’s Department of Statistics.
Environmental epidemiologyApplied research
Misspecified seasonality adjustment in temperature and preterm birth studies may introduce severe bias: a real birth cohort-based simulation
Published with S. Girgin, O. Erez, and R. Raz in International Journal of Epidemiology.
Networks & interferenceMethods & theory
Sensitivity analysis for contamination in egocentric-network randomized trials
Work led by PhD student Bar Weinstein. Published in Biostatistics.
Networks & interference · Generalizability
Transportability under network interference at JSM 2026
Presented “Transportability and Generalizability of Causal Effects Under Network Interference” at the Joint Statistical Meetings in Boston.
Generalizability · Infectious diseasesMethods & theory
Estimating treatment effects from non-overlapping cohorts
Work led by PhD student Avi Baraz. Published with A. Cahan, T. Brosh-Nissimov, M. Chowers, and U. Obolski in BMC Medical Research Methodology.
Survival & event timeMethods & theory
Censoring, competing events, and multistate models
A comment with M. Gorfine appeared in Biometrical Journal.
Survival & event time · Principal stratificationMethods & theory
Causal effects on non-terminal event time
Work led by PhD student Tamir Zehavi. Published with U. Obolski and M. Chowers in Statistics in Medicine.
Congratulations to Noam Tetelman for defending her MSc thesis!
Her thesis is titled Identifying Influential Individuals in a Causal Network Through Random Effects Framework.
Negative controls · EconometricsMethods & theory
Negative-control falsification tests for instrumental-variable designs
Published with O. Danieli, I. Walk, B. Weinstein, and D. Zeltzer in the American Economic Review.
Congratulations to Rachel Axelrod for completing her PhD!
Her dissertation is titled Topics in Causal Inference with Applications to Medical Data.