Tutorials

Basic causal discovery models without latent confounders

The first tutorial presents several algorithms for causal discovery without latent confounding: the Peters and Clarke (PC) algorithm. These models provide a basis for learning causal structure from data when we make the assumption that there are no latent confounders.

• PC algorithm for causal discovery from observational data without latent confounders