Wrapping it up
This chapter introduced us to the three basic conditional independence structures – chains, forks, and colliders (the latter also known as immoralities or v-structures). We studied the properties of these structures and demonstrated that colliders have unique properties that make constraint-based causal discovery possible. We discussed how to deal with cases when it’s impossible to orient all the edges in a graph and introduced the concept of MECs. Finally, we got our hands dirty with coding the examples of all the structures and analyzed their statistical properties using multiple linear regression.
This chapter concludes the first, introductory part of this book. The next chapter starts on the other side, in the fascinating land of causal inference. We’ll go beyond simple linear cases and see a whole new zoo of models.
Ready?
