Matteo Bonvini

Matteo Bonvini is an Assistant Professor in the Department of Statistics at Rutgers University. He holds an office in Hill Center 401, located at 110 Frelinghuysen Road, Piscataway, NJ 08854. His research focuses on causal inference, nonparametric methods, and semiparametric efficiency theory, with applications to healthcare and epidemiology.

Dr. Bonvini earned his Ph.D. in Statistics from Carnegie Mellon University. His work bridges theoretical advancements in causal modeling with practical applications in observational studies and clinical research. Recent research highlights include developing sensitivity analysis frameworks for unmeasured confounding and optimizing subgroup identification in treatment effect estimation.

His publications span topics such as instrumental variable methods, incremental causal effects, and causal inference in pandemic studies. He has contributed to healthcare analytics through studies on surgical outcomes and ICU predictive modeling. His methodological work emphasizes statistical rigor while addressing real-world complexities in observational data.

Bonvini's affiliations include the School of Arts and Sciences (implied via Rutgers' structure) and collaborations with healthcare institutions. He maintains an active research program with grants likely supporting his work in causal inference methodologies, though specific grants are not detailed here.