Wrapping it up

We started this chapter by refreshing our knowledge of graphs and learned how to build simple graphs using Python and the NetworkX library. We introduced GCMs and DAGs and discussed some common limitations and challenges that we might face when using them.

Finally, we examined selected approaches to model causal systems with cycles.

Now you have the ability to translate between the visual representation of a graph and an adjacency matrix. The basic DAG toolkit that we’ve discussed in this chapter will allow you to work smoothly with many causal inference and causal discovery tools and will help you represent your own problems as graphs, which can bring a lot of clarity – even in your work with traditional (non-causal) machine learning.

The knowledge you gained in this chapter will be critical to understanding the next chapter and the next two parts of this book. Feel free to review this chapter anytime you need.

In the next chapter, we’...