IRES AI-PROWIL Keynote Talk
DATE: Tuesday, September 8, 2026, 5-6 PM
SPEAKER: Dr. Alex Nikolaev, Associate Professor and Director of the Social Optimization Laboratory at the Department of Industrial and Systems Engineering (University at Buffalo)
TITLE: Optimization and AI for Quasi-Experimental Causal Inference
IN PERSON: Simone Center 1600 Atrium
ABSTRACT: Scientists across numerous disciplines attempt to identify and document causal relationships. Those unable to design and implement randomized (control) experiments to collect experimental data must resort to observational studies. To make causal inferences outside the experimental realm, researchers attempt to post-process large observational data sets to mimic experimental data. Manipulating the collected data to resemble experimental data is a challenging problem. Research in this field has enjoyed over forty years of success, focusing primarily on regression-based propensity score estimation and matching of individual treated and untreated (control) units, toward estimating treatment effects. This talk overviews the ideas and research directions more recently brought to the field by optimization and AI, from balance-optimal subset selection to neural balancing representation methods, highlighting their rewards and challenges.
BIO: Dr. Alexander Nikolaev is an Associate Professor and Director of the Social Optimization Laboratory at the Department of Industrial and Systems Engineering at University at Buffalo (SUNY). His interests and expertise are in stochastic modeling, optimization-driven causal inference, social network analysis, and healthcare informatics. He was a (co-)recipient of INFORMS Impact Prize and multiple National Science Foundation Awards. His current research focuses on the development of quantitative models to support decision aids in healthcare.
Event Snapshot
When and Where
Who
Open to the Public
| Cost | FREE |
Interpreter Requested?
No