arXiv research

New method learns DAGs from data without acyclicity constraint.

This supplementary material includes three parts: some preliminary results, four examples, an experiment, three new algorithms, and all proofs of the results in the paper "Reversible MCMC on Markov equivalence classes of sparse directed acyclic graphs".

The paper tackles learning varying DAG structures based on contextual features.

DAGgr aggregates multiple DAGs to stabilize causal structure learning.

ENCOD learns causal graphs efficiently without acyclicity constraints.

We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning. Generating graphs with specified structures is an important and challenging task in various application fields, however most current graph generation methods produce graphs with undirected edg…

ProDAG uses variational inference to learn DAGs with uncertainty quantification.

Neural networks with DAGs show linearity as width increases.

Develops a new method for learning non-parametric DAGs using RKHS.

Study of Betti numbers in prodsimplicial complexes for directed graphs, focusing on DNA recombination.

Paper proposes DAG-DB for learning discrete DAGs via backpropagation.

The paper shows that relaxing assumptions about causal graphs can lead to exponentially large equivalence classes.

Paper proposes methods to learn DAGs from partial orderings.

COSMO learns DAG structure without acyclicity constraints.

PIVID infers DAG structures from data using variational inference and permutations.

Paper constructs unfaithful probability distributions in binary causal graphs.

Unified framework for clustering and learning causal graphs across subjects.

DAGSurv uses deep neural networks to analyze survival data based on causal graphs.

Proposes an approach to ensure acyclic graphs in Bayesian structure learning.

We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables. LDAGs generalize earlier proposals for allowing local structures in the conditional probability distribution of a node, such that unrestricted label sets determine which edges can be deleted from the underl…

ALIAS uses RL to learn DAGs without acyclicity constraints.

This paper considers the problem of estimating the structure of multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs, that does not require that the ver…

This dissertation uses ILP to learn Bayesian network structures efficiently.

Identifies causal effects in partially directed acyclic graphs with observed variables.

Directed acyclic graphs (DAGs) are a popular framework to express multivariate probability distributions. Acyclic directed mixed graphs (ADMGs) are generalizations of DAGs that can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables im…

We explore non-acyclic GFlowNets in discrete settings.

The paper proposes using low rank assumption to improve causal structure learning in DAGs.

Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of t…

CaTs use DAGs with transformers to enforce causal constraints, improving neural network robustness.

Directed graphs occur throughout statistical modeling of networks, and exchangeability is a natural assumption when the ordering of vertices does not matter. There is a deep structural theory for exchangeable undirected graphs, which extends to the directed case via measurable objects known as digraphons. Using digraph…

We propose two spectral algorithms for partitioning nodes in directed graphs respectively with a cyclic and an acyclic pattern of connection between groups of nodes. Our methods are based on the computation of extremal eigenvalues of the transition matrix associated to the directed graph. The two algorithms outperform …

This work presents entropic constraints from DAGs with hidden variables.

We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an …

We investigate probabilistic graphical models that allow for both cycles and latent variables. For this we introduce directed graphs with hyperedges (HEDGes), generalizing and combining both marginalized directed acyclic graphs (mDAGs) that can model latent (dependent) variables, and directed mixed graphs (DMGs) that c…

Causal inference relies on the structure of a graph, often a directed acyclic graph (DAG). Different graphs may result in different causal inference statements and different intervention distributions. To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID). T…

Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study dee…

New method learns graph structure with hidden causes from observational data.

This work improves policy-based training by proposing an evaluation balance objective for GFlowNets.

Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning…

Study counterfactuals in cyclic systems with shifts and scales.

The sizes of Markov equivalence classes of directed acyclic graphs play important roles in measuring the uncertainty and complexity in causal learning. A Markov equivalence class can be represented by an essential graph and its undirected subgraphs determine the size of the class. In this paper, we develop a method to …

We consider structural equation models in which variables can be written as a function of their parents and noise terms, which are assumed to be jointly independent. Corresponding to each structural equation model, there is a directed acyclic graph describing the relationships between the variables. In Gaussian structu…

New distances for causal graphs improve evaluation of learned structures.

Obtaining continuous representations of structural data such as directed acyclic graphs (DAGs) has gained attention in machine learning and artificial intelligence. However, embedding complex DAGs in which both ancestors and descendants of nodes are exponentially increasing is difficult. Tackling in this problem, we de…

Deep Gaussian Processes model functions on DAGs with partially observed data.

In this note we derive enumerative formulas for several types of labelled acyclic directed graphs by slight modifications of the familiar recursive formula for simple acyclic digraphs. These considerations are motivated by, and based upon, recent combinatorial results in geometric topology obtained by S.Choi, who estab…

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…

New method learns DAGs from noisy data without identifiability assumptions.