The discovery of causal relationships is a fundamental problem in science and
medicine. In recent years, many elegant approaches to discovering causal
relationships between two variables from observational data have been proposed.
However, most of these deal only with purely directed causal relationships and
cannot detect latent common causes. Here, we devise a general heuristic which
takes a causal discovery algorithm that can only distinguish purely directed
causal relations and modifies it to also detect latent common causes. We apply
our method to two directed causal discovery algorithms, the Information
Geometric Causal Inference of (Daniusis et al., 2010) and the Kernel
Conditional Deviance for Causal Inference of (Mitrovic, Sejdinovic, & Teh,
2018), and extensively test on synthetic data -- detecting latent common causes
in additive, multiplicative and complex noise regimes -- and on real data,
where we are able to detect known common causes. In addition to detecting
latent common causes, our experiments demonstrate that both the modified
algorithms preserve the performance of the original in distinguishing directed
causal relations.

本文提出了一种探测变量之间潜在共同原因的因果发现算法，并将其应用于两个有向因果发现算法的实验中。结果表明，该算法可以探测到已知的共同原因，并保留原始算法在区分有向因果关系方面的性能