Causal Representation Learning

This chapter introduces causal representation learning as an inverse problem: recovering a latent causal model from the observations it generates. It reviews how interventions on latent variables can make this recovery possible, covering identifiability results for parametric and nonparametric models under interventions on one or multiple variables. The chapter also discusses practical algorithms that connect these theoretical results to representation learning.