Member-only story
Causal Discovery
Learning causation from data using Python
This is the final post in a series of three on causality. In previous posts, the “new science” [1] of causality was introduced, and the topic of causal inference was discussed. The focus of this article is a related idea, causal discovery. I will start with a description of causal discovery, sketch how it works, and conclude with a concrete example using Python.
What is Causal Discovery?
In the previous post, I discussed causal inference, which aims to answer questions involving cause and effect. Although causal inference is a powerful tool, it requires a key. Namely, it requires a causal model. Often, in the real world, however, we may not be sure about which variables cause each other. This is where causal discovery can help.
Causal discovery aims to infer causal structure from data. In other words, given a dataset, derive a causal model that describes it.
