Causal discovery – real-world applications, challenges, and open problems

Before we wrap up this chapter, let’s take a broader perspective and discuss the applicability of causal discovery to real-world problems and challenges that may arise along the way.

In the previous chapter, we mentioned that Alexander Reisach and colleagues have demonstrated that the synthetic data used to evaluate causal discovery methods might contain unintended regularities that can be relatively easily exploited by these models (Reisach et al., 2021). The problem is that these regularities might not be present in real-world data.

Another challenge is that real-world data with a known causal structure is scarce. This makes synthetic datasets a natural benchmarking choice, yet this choice leaves us without a clear understanding of what to expect of causal structure learning algorithms when applied to real-world datasets.

The lack of reliable benchmarks is one of the main challenges in...