We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

Causal discovery becomes especially challenging when the possibility of latent confounding
and/or selection bias is not assumed away. For this task, ancestral graph models are …

We consider the problem of learning causal structures with latent variables using
interventions. Our objective is not only to learn the causal graph between the observed …

We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more
general types of graphs that describe Markov equivalence classes of DAGs and/or allow for …

This work considers the problem of learning linear Bayesian networks when some of the
variables are unobserved. Identifiability and efficient recovery from low-order observable …

We consider testing and learning problems on causal Bayesian networks as defined by
Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables …

Great efforts have been devoted to causal discovery from observational data, and it is well
known that introducing some background knowledge attained from experiments or human …

We develop terminology and methods for working with maximally oriented partially directed
acyclic graphs (maximal PDAGs). Maximal PDAGs arise from imposing restrictions on a …

Great efforts have been devoted to causal discovery from observational data, and it is well
known that introducing some background knowledge attained from experiments or human …

In recursive linear models, the multivariate normal joint distribution of all variables exhibits a
dependence structure induced by a recursive (or acyclic) system of linear structural …