Part 3: Causal Discovery
In Part 3, we will start our journey into the world of causal discovery. We will begin with an overview of the sources of causal knowledge and a deeper look at important assumptions.
We will introduce four families of causal discovery algorithms and implement them using gCastle. We will move toward advanced methods and demonstrate how to train a DECI algorithm using PyTorch.
Along the way, we will show you how to inject expert knowledge into the causal discovery process, and we will briefly discuss methods that allow us to combine observational and interventional data to learn causal structure more efficiently.
We will close Part 3 with a summary of the book, a discussion of causality in business, a sneak peek into the (potential) future of the field, and pointers to more resources on causal inference and discovery for those who are ready to continue their causal journey.
This part comprises the following chapters:
- Chapter 12, Can...
