Wisdom Of The Week

My interest in formal causal analysis was seeded a couple of years ago, with a reading group that was dedicated to Judea Pearl’s work. We didn’t get very far, as I was a bit disappointed with what causal calculus can and cannot do. This may have been because I didn’t come in with the right expectations – I expected a black box that automatically finds causes. Recently reading Samantha Kleinberg’s excellent book Why: A Guide to Finding and Using Causes has made my expectations somewhat more realistic:

Thousands of years after Aristotle’s seminal work on causality, hundreds of years after Hume gave us two definitions of it, and decades after automated inference became a possibility through powerful new computers, causality is still an unsolved problem. Humans are prone to seeing causality where it does not exist and our algorithms aren’t foolproof. Even worse, once we find a cause it’s still hard to use this information to prevent or produce an outcome because of limits on what information we can collect and how we can understand it. After looking at all the cases where methods haven’t worked and researchers and policy makers have gotten causality really wrong, you might wonder why you should bother.
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Rather than giving up on causality, what we need to give up on is the idea of having a black box that takes some data straight from its source and emits a stream of causes with no need for interpretation or human intervention. Causal inference is necessary and possible, but it is not perfect and, most importantly, it requires domain knowledge.

Kleinberg’s book is a great general intro to causality, but it intentionally omits the mathematical details behind the various methods. I am now ready to once again go deeper into causality, perhaps starting with Kleinberg’s more technical book, Causality, Probability, and Time. Other recommendations are very welcome!

- Why you should stop worrying about deep learning and deepen your understanding of causality instead