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

Alexander G. Nikolaev (), Sheldon H. Jacobson (), Wendy K. Tam Cho (), Jason J. Sauppe () and Edward C. Sewell ()
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Alexander G. Nikolaev: Department of Industrial and Systems Engineering, University at Buffalo (SUNY), Buffalo, New York 14260
Sheldon H. Jacobson: Department of Computer Science, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801
Wendy K. Tam Cho: Departments of Political Science and Statistics and the National Center for Supercomputing Applications, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801
Jason J. Sauppe: Department of Computer Science, University of Illinois at Urbana--Champaign, Urbana, Illinois 61801
Edward C. Sewell: Department of Mathematics and Statistics, Southern Illinois University Edwardsville, Edwardsville, Illinois 62026

Operations Research, 2013, vol. 61, issue 2, 398-412

Abstract: Scientists in all disciplines 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 make causal inferences outside the experimental realm, researchers attempt to control for bias sources by postprocessing observational data. Finding the subset of data most conducive to unbiased or least biased treatment effect estimation is a challenging, complex problem. However, the rise in computational power and algorithmic sophistication leads to an operations research solution that circumvents many of the challenges presented by methods employed over the past 30 years.

Keywords: causal inference; balance optimization; subset selection (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (10)

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