Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data

Sheldon H. Jacobson is a Professor and Director of the Simulation and Optimization Laboratory at the University of Illinois. He has a broad set of basic and applied research interests, including problems related to optimal decision-making, national security, and public health. His research has been disseminated in numerous archival journals, including Operations Research, Mathematical Programming, and SIAM Journal on Control and Optimization, among others. He has been recognized with several national awards, including a Best Paper Award in IIE Transactions Focused Issue on Operations Engineering (2003), a Guggenheim Fellowship (2003), the Outstanding IIE Publication Award (2009), and the IIE Award for Technical Innovation in Industrial Engineering (2010). His research has been supported by grants from the National Science Foundation and the Air Force Office of Scientific Research. He is a Fellow of INFORMS and IIE.

Researchers in medicine and the social sciences attempt to identify and document causal relationships. Those not fortunate enough to be able to design and implement randomized control trials must resort to observational studies. To preserve the ability to make causal inferences outside the experimental realm, researchers attempt to post-process observational data to draw meaningful insights and conclusions. Finding the subset of data that most closely resembles experimental data is a challenging, complex problem. However, the rise in computational power and discrete optimization algorithmic advances suggests an operations research solution as an alternative to methods currently being employed.

Joint work with Jason J. Sauppe (University of Illinois at Urbana-Champaign)

Event Topic

Data Science

Discrete Applied Math Seminar