Causal Discovery from Data in the Presence of Selection Bias
Causal Discovery from Data in the Presence of Selection Bias
复制标题
在存在选择偏差的情况下从数据中发现因果关系
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Gregory F. Cooper
中科院分区：
文献类型：
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作者：
Gregory F. Cooper
最近的研究进展使得考虑使用观测数据来推断测量变量之间的因果关系成为可能。选择偏差来自于对实体的观察，这些实体不代表由感兴趣的因果过程产生的实体。本文表明，我们有时可以在观测数据中检测到选择偏差的存在。本文还演示了选择偏差如何阻碍从观测数据中发现因果关系。正如我们将描述的那样，使用实验数据（例如，来自随机对照试验的数据）来发现因果关系可能也容易受到涉及选择偏差的问题的影响。我们就如何在选择偏差的情况下进行因果发现提出了建议。
Recent research advances have made it possible to consider using observational data to infer causal relationships among measured variables. Selection bias results from the observation of entities that are not representative of the entities that are generated by a causal process of interest. This paper shows that we can sometimes detect the presence of selection bias in observational data. The paper also demonstrates how selection bias can hinder the discovery of causal relationships from observational data. As we will describe, the use of experimental data (e.g., daa from randomized, controlled trials) to discover causal relationships can be susceptible as well to problems involving selection bias. We offer suggestions for how to proceed with causal discovery in the face of selection bias.