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Causal Diagram: Confronting the Achilles’ Heel in Observational Data
“The Book of Why” Chapters 3&4, a Read with Me series
In my previous two articles, I kicked off the “Read with Me” series and finished reading the first two chapters from “The Book of Why” by Judea Pearl. These articles discuss the necessity of introducing causality in enabling human-like decision-making and emphasize the Ladder of Causation that sets up the foundation for future discussions. In this article, we will explore the keyholes that open the door from the first to the second rung of the ladder of causation, allowing us to move beyond probability and into causal thinking. We will go from Bayes’s rule to the Bayesian network to, finally, the causal diagrams.
From Bayes’s rule to inverse probability
As a fan of detective novels, my favorite series is Sherlock Holmes. I still remember all these days and nights I read them without noticing time passing by. Years later, lots of the case details had already disappeared from my memories, but I still remember the famous quotes like everyone else:
When you have eliminated the impossible, whatever remains, however improbable, must be the truth.
