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
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…