A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias
A constraint-based algorithm for causal discovery with cycles, latent variables and selection bias
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一种基于约束的因果发现算法，具有循环、潜在变量和选择偏差
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发表时间：
2018
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通讯作者：
Eric V. Strobl
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作者：
Eric V. Strobl
自然界中的因果过程可能包含循环，而真实的数据集可能违反因果充分性并包含选择偏差。目前没有基于约束的因果发现算法可以同时处理周期、潜变量和选择偏差（CLS）。因此，我介绍了一种算法，称为循环因果推理（CCI），使健全的推论与CLS下的条件独立的预言，只要我们可以表示为一个非递归的线性结构方程模型的非递归的线性结构方程模型的因果循环过程中的独立错误。实验结果表明，CCI优于循环因果发现算法在循环的情况下，以及竞争对手的快速因果推理和真正的快速因果推理算法在非循环的情况下。R实现可在https://github.com/ericstrobl/CCI上获得。
Causal processes in nature may contain cycles, and real datasets may violate causal sufficiency as well as contain selection bias. No constraint-based causal discovery algorithm can currently handle cycles, latent variables and selection bias (CLS) simultaneously. I therefore introduce an algorithm called cyclic causal inference (CCI) that makes sound inferences with a conditional independence oracle under CLS, provided that we can represent the cyclic causal process as a non-recursive linear structural equation model with independent errors. Empirical results show that CCI outperforms the cyclic causal discovery algorithm in the cyclic case as well as rivals the fast causal inference and really fast causal inference algorithms in the acyclic case. An R implementation is available at https://github.com/ericstrobl/CCI.