116 citations · 267 across the 30 of their papers we have counts for
16 papers · 1 filter
Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
Jose M. Peña
An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we p…
Identifiability of Gaussian Structural Equation Models with Dependent Errors Having Equal Variances
Jose M. Peña
In this paper, we prove that some Gaussian structural equation models with dependent errors having equal variances are identifiable from their corresponding Gaussian distributions.…
Unifying DAGs and UGs
Jose M. Peña
We introduce a new class of graphical models that generalizes Lauritzen-Wermuth-Frydenberg chain graphs by relaxing the semi-directed acyclity constraint so that only directed cycl…
Representing Independence Models with Elementary Triplets
Jose M. Peña
In an independence model, the triplets that represent conditional independences between singletons are called elementary. It is known that the elementary triplets represent the ind…
Causal Effect Identification in Acyclic Directed Mixed Graphs and Gated Models
Jose M. Peña, Marcus Bendtsen
We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models…
Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs
Jose M. Peña
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…
Factorization, Inference and Parameter Learning in Discrete AMP Chain Graphs
Jose M. Peña
We address some computational issues that may hinder the use of AMP chain graphs in practice. Specifically, we show how a discrete probability distribution that satisfies all the i…
Every LWF and AMP chain graph originates from a set of causal models
Jose M. Peña
This paper aims at justifying LWF and AMP chain graphs by showing that they do not represent arbitrary independence models. Specifically, we show that every chain graph is inclusio…
Error AMP Chain Graphs
Jose M. Peña
Any regular Gaussian probability distribution that can be represented by an AMP chain graph (CG) can be expressed as a system of linear equations with correlated errors whose struc…
Marginal AMP Chain Graphs
Jose M. Peña
We present a new family of models that is based on graphs that may have undirected, directed and bidirected edges. We name these new models marginal AMP (MAMP) chain graphs because…
Learning AMP Chain Graphs and some Marginal Models Thereof under Faithfulness: Extended Version
Jose M. Peña
This paper deals with chain graphs under the Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP chain graph…
Learning AMP Chain Graphs under Faithfulness
Jose M. Peña
This paper deals with chain graphs under the alternative Andersson-Madigan-Perlman (AMP) interpretation. In particular, we present a constraint based algorithm for learning an AMP…
Towards Optimal Learning of Chain Graphs
Jose M. Peña
In this paper, we extend Meek's conjecture (Meek 1997) from directed and acyclic graphs to chain graphs, and prove that the extended conjecture is true. Specifically, we prove that…
Finding Consensus Bayesian Network Structures
Jose M. Peña
Suppose that multiple experts (or learning algorithms) provide us with alternative Bayesian network (BN) structures over a domain, and that we are interested in combining them into…
Reading Dependencies from Covariance Graphs
Jose M. Peña
The covariance graph (aka bi-directed graph) of a probability distribution is the undirected graph where two nodes are adjacent iff their corresponding random variables are…
Faithfulness in Chain Graphs: The Gaussian Case
Jose M. Peña
This paper deals with chain graphs under the classic Lauritzen-Wermuth-Frydenberg interpretation. We prove that the regular Gaussian distributions that factorize with respect to a…