Leveraging Directed Causal Discovery to Detect Latent Common Causes in Cause-Effect Pairs

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 (IGCI) of (Daniusis et al., 2010) and the kernel conditional deviance for causal inference of (Mitrovic et al., 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.

Automated summary

The article introduces a new method for detecting hidden common causes in causal relationships. The method modifies existing algorithms to be able to distinguish both purely directed causal relationships and latent common causes. Through testing on synthetic and real data, the effectiveness and performance of this method are demonstrated.