Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
含隐蔽混杂的循环模型因果发现方法比较AI 解读 ↓
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含隐蔽混杂的循环模型因果发现方法比较
- 研究问题
- 当系统存在反馈循环和未观测混杂时，哪些因果发现方法能够用于稀疏线性模型，其表现如何随干预设置和样本量变化？
- 方法与思路
- 作者比较两种 LLC 方法和两种基于 ASP 的算法，在允许循环及隐蔽混杂的稀疏线性模型中改变干预安排与数据规模。
- 材料陈述的贡献
- 摘要提供了四种方法的系统比较框架，考察多种干预情形和样本量下的表现，但没有报告具体方法的优势或优劣排序。
- 阅读关注点
- 应核对各算法需要的模型与干预假设、评价对象和指标，以及不同实验设置是否公平反映反馈与隐蔽混杂带来的困难。
- 阅读边界
- 这是预印本，同行评审或期刊接收未经核实。摘要未提供比较结果、识别范围或运行成本，不能据此选择表现最好的算法。
原文摘要
Nowadays, the need for causal discovery is ubiquitous. A better understanding of not just the stochastic dependencies between parts of a system, but also the actual cause-effect relations, is essential for all parts of science. Thus, the need for reliable methods to detect causal directions is growing constantly. In the last 50 years, many causal discovery algorithms have emerged, but most of them are applicable only under the assumption that the systems have no feedback loops and that they are causally sufficient, i.e. that there are no unmeasured subsystems that can affect multiple measured variables. This is unfortunate since those restrictions can often not be presumed in practice. Feedback is an integral feature of many processes, and real-world systems are rarely completely isolated and fully measured. Fortunately, in recent years, several techniques, that can cope with cyclic, causally insufficient systems, have been developed. And with multiple methods available, a practical application of those algorithms now requires knowledge of the respective strengths and weaknesses. Here, we focus on the problem of causal discovery for sparse linear models which are allowed to have cycles and hidden confounders. We have prepared a comprehensive and thorough comparative study of four causal discovery techniques: two versions of the LLC method [10] and two variants of the ASP-based algorithm [11]. The evaluation investigates the performance of those techniques for various experiments with multiple interventional setups and different dataset sizes.