Wrapping it up
We started this chapter with a refresher on important causal discovery assumptions. We then introduced gCastle. We discussed the library’s main modules and trained our first causal discovery algorithm. Next, we discussed the four main families of causal discovery models – constraint-based, score-based, functional, and gradient-based – and implemented at least one model per family using gCastle. Finally, we ran a comparative experiment and learned how to pass expert knowledge to causal models.
In the next chapter, we’ll discuss more advanced ideas in causal discovery and take a broader perspective on the applicability of causal discovery methods in real-life use cases.
Ready for one more dive?
