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 …

Inferring causal structure poses a combinatorial search problem that typically involves
evaluating structures with a score or independence test. The resulting search is costly, and …

This article presents an overview of several known approaches to causal discovery. It is
organized by relating the different fundamental assumptions that the methods depend on …

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a
continuous optimization problem by leveraging an algebraic characterization of acyclicity …

We study the problem of causal structure learning when the experimenter is limited to
perform at most $ k $ non-adaptive experiments of size $1 $. We formulate the problem of …

Many of the causal discovery methods rely on the faithfulness assumption to guarantee
asymptotic correctness. However, the assumption can be approximately violated in many …

Counting and uniform sampling of directed acyclic graphs (DAGs) from a Markov
equivalence class are fundamental tasks in graphical causal analysis. In this paper, we …

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 …

Causal structure learning is a key problem in many domains. Causal structures can be learnt
by performing experiments on the system of interest. We address the largely unexplored …

Enumerating the directed acyclic graphs (DAGs) of a Markov equivalence class (MEC) is an
important primitive in causal analysis. The central resource from the perspective of …