Wrapping it up
In this chapter, we introduced the concept of the Ladder of Causation. We discussed each of the three rungs of the ladder: associations, interventions, and counterfactuals. We presented mathematical apparatus to describe each of the rungs and translated the ideas behind them into code. These ideas are foundational for causal thinking and will allow us to understand more complex topics further on in the book.
Additionally, we broadened our perspective on causality by discussing the relationships between causality and various families of machine learning algorithms.
In the next chapter, we’ll take a look at the link between observations, interventions, and linear regression to see the differences between rung one and rung two from yet another perspective. Ready?
