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

• 成果类型: Article
• 署名作者: Nikolaev, Alexander G.; Jacobson, Sheldon H.; Cho, Wendy K. Tam; Sauppe, Jason J.; Sewell, Edward C.
• 署名单位: State University of New York (SUNY) System; University at Buffalo, SUNY; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign; Southern Illinois University System; Southern Illinois University Edwardsville
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1120.1118
• 发表日期: 2013
• 页码: 398-412
• 关键词: propensity score; MULTIVARIATE; BIAS

摘要

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.