Wrapping it up
In this chapter, we talked about the challenges that we face while using causal inference methods in practice. We discussed important assumptions and proposed potential solutions to some of the discussed challenges. We got back to the topic of confounding and showed examples of selection bias.
The four most important concepts from this chapter are identifiability, the positivity assumption, modularity, and selection bias.
Are you ready to add some machine learning sauce to all we’ve learned so far?
