论文标题
选择偏见下的基于本地约束的因果发现
Local Constraint-Based Causal Discovery under Selection Bias
论文作者
论文摘要
我们认为除了混淆外，还存在从独立性约束选择偏见中发现因果关系的问题。尽管在此设置中，开创性的FCI算法是合理的且完整的，但目前已知尚无针对其输出的因果解释的标准。相反，我们专注于独立关系的本地模式，在该模式中，我们没有发现只有三个变量的声音方法，可以包括背景知识。 Y结构模式在预测可能存在周期的数据下的数据中预测因果关系时表现出声音。我们引入了Y结构的有限样本评分规则，该规则被证明可以成功预测包括选择机制的模拟实验中的因果关系。在现实世界中的微阵列数据上，我们表明Y结构变体在不同数据集中的性能很好，可能会由于选择偏差而导致伪造的相关性。
We consider the problem of discovering causal relations from independence constraints selection bias in addition to confounding is present. While the seminal FCI algorithm is sound and complete in this setup, no criterion for the causal interpretation of its output under selection bias is presently known. We focus instead on local patterns of independence relations, where we find no sound method for only three variable that can include background knowledge. Y-Structure patterns are shown to be sound in predicting causal relations from data under selection bias, where cycles may be present. We introduce a finite-sample scoring rule for Y-Structures that is shown to successfully predict causal relations in simulation experiments that include selection mechanisms. On real-world microarray data, we show that a Y-Structure variant performs well across different datasets, potentially circumventing spurious correlations due to selection bias.
