Enhancing Identification of Causal Effects by Pruning
Enhancing Identification of Causal Effects by Pruning
复制标题
通过修剪增强因果效应的识别
DOI：
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发表时间：
2018
影响因子：
6
通讯作者：
J. Karvanen
中科院分区：
文献类型：
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作者：
S. Tikka;J. Karvanen
因果模型传达了我们对现实世界中原因和影响的假设。利益通常在于识别行动的效果，这意味着从观察到的概率分布中得出表达式的介入概率分布，以造成该动作导致的介入分布。在许多情况下，可识别性算法可能会返回复杂的表达式，其中包含实际上不必要的变量。实际上，在处理测量错误或丢失数据时，这可能会导致额外的计算负担，并增加估计的偏差或效率低下。我们提出了图形标准，以检测在识别因果效应方面多余的变量。我们还提供了实现这些标准的众所周知可识别性算法的改进版本。
Causal models communicate our assumptions about causes and effects in real-world phe- nomena. Often the interest lies in the identification of the effect of an action which means deriving an expression from the observed probability distribution for the interventional distribution resulting from the action. In many cases an identifiability algorithm may return a complicated expression that contains variables that are in fact unnecessary. In practice this can lead to additional computational burden and increased bias or inefficiency of estimates when dealing with measurement error or missing data. We present graphical criteria to detect variables which are redundant in identifying causal effects. We also provide an improved version of a well-known identifiability algorithm that implements these criteria.