In this review, we discuss approaches for learning causal structure from data, also called
causal discovery. In particular, we focus on approaches for learning directed acyclic graphs …

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 …

Causal reasoning is primarily concerned with what would happen to a system under
external interventions. In particular, we are often interested in predicting the probability …

Constraint-based causal discovery methods leverage conditional independence tests to
infer causal relationships in a wide variety of applications. Just as the majority of machine …

We consider the task of learning a causal graph in the presence of latent confounders given
iid samples from the model. While current algorithms for causal structure discovery in the …

Abstract We investigate Greedy PAG Search (GPS) for score-based causal discovery over
equivalence classes, similar to the famous Greedy Equivalence Search algorithm, except …

Causal effect identification is a fundamental task in artificial intelligence. A most ideal
scenario for causal effect identification is that there is a directed acyclic graph as a prior …

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 …

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 compare score-based and constraint-based learning in the presence of latent
confounders. We use a greedy search strategy to identify the best fitting maximal ancestral …