Causal representation learning as causal discovery

Causal representation learning as causal discovery

This document highlights directions in causal representation learning in the context of causal discovery.

Causal representation learning is a broad area, encompassing many types of data. In dodiscover, the focus is on multivariate set of data that lives naturally in a Pandas DataFrame, in which rows correspond to sample observations and columns to variables.

In many cases learning a representation is in service of a downstream goal like prediction. To that end, some libraries treat the representation as a low-level artifact and secondary abstraction. In constrast, dodiscovery treats the representation as a primary abstraction and provides an API for manipulating, visualizing and using representations with other tasks.