Fast causal orientation learning in directed acyclic graphs

Signed directed acyclic graphs for causal inference.

Formal rules governing signed edges on causal directed acyclic graphs are described in this paper and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an inter...

Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs

Alternative acyclic directed mixed graphs (ADMGs) are graphs that may allow causal effect identification in scenarios where Pearl’s original ADMGs may not, and vice versa. Therefore, they complement each other. In this paper, we introduce a sound algorithm for identifying arbitrary causal effects from alternative ADMGs. Moreover, we show that the algorithm is complete for identifying the causal...

Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs

We extend Andersson-Madigan-Perlman chain graphs by (i) relaxing the semidirected acyclity constraint so that only directed cycles are forbidden, and (ii) allowing up to two edges between any pair of nodes. We introduce global, and ordered local and pairwise Markov properties for the new models. We show the equivalence of these properties for strictly positive probability distributions. We also...

Generic Directed Acyclic Graphs

Copula directed acyclic graphs

We present a new methodology for selecting a Bayesian network for continuous data outside the widely used class of multivariate normal distributions. The use of bivariate copula densities to model connections between pairs of nodes in the network brings in flexibility in the choice of the distribution. Cand D-vines play a central role in dealing with the high-dimensional aspect of the data, due...