Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data
Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data
复制标题
平衡优化子集选择（BOSS）：利用观察数据进行因果推理的替代方法
DOI：
10.1287/opre.1120.1118
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
E. Sewell
中科院分区：
文献类型：
--
作者：
Alexander G. Nikolaev;S. Jacobson;W. Cho;Jason J. Sauppe;E. Sewell
所有学科的科学家都试图确定和记录因果关系。那些没有足够幸运能够设计和实施随机对照试验的人必须求助于观察性研究。为了在实验领域之外进行因果推断，研究人员试图通过后处理观察数据来控制偏差来源。寻找最有利于无偏或最小偏倚治疗效果估计的数据子集是一个具有挑战性的复杂问题。然而，计算能力和算法复杂性的提高导致了运筹学解决方案，该解决方案规避了过去30年来采用的方法所带来的许多挑战。
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.