Efficiently discovers causal DAG permutations without additional assumptions.
problem Learning a DAG up to Markov equivalence.
method Utilizing DAG-specific problem structure, introduces an efficient algorithm for sparse permutations.
result Significant improvement in permutation discovery compared to existing algorithms.
New method learns DAGs from data without acyclicity constraint.
problem Learning DAGs from data without imposing acyclicity.
method Sparse matrix factorization and ℓ1-penalized optimization. result Empirical success in recovering true graphs and almost-DAG graphs.
The paper tackles learning varying DAG structures based on contextual features.
problem Learning a single DAG for the entire population from observational data.
method A neural network that maps contextual features to a weighted adjacency matrix of a DAG, with a projection layer to ensure acyclicity.
result The new approach can recover context-specific DAGs where existing methods fail.
In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) from a list of base classifiers provided by an external learning method such as AdaBoost. The basic idea is to cast the DAG design task as a Markov decision process. Each instance can decide to use or to skip each base clas…
We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data. Our approach is based on a recent algebraic characterization of DAGs that led to a fully continuous program for score-based learning of DAG models parametrized by a linear structural equation model (SEM). We extend this a…
New pruning method for sparse additive models speeds up causal structure learning.
problem Efficiently prune spurious edges from fully-connected DAG induced by estimated topological order.
method Sparse additive models combined with randomized tree embedding and group-wise sparse regression.
result Significantly faster than existing pruning methods while maintaining comparable accuracy.
A new method learns DAGs from Gaussian data without verifying acyclicity.
problem Learning DAGs from Gaussian data without verifying acyclicity.
method Relaxation technique for permutation matrix estimation and cyclic coordinatewise descent for sparse Cholesky factor estimation.
result The method recovers DAGs without verifying acyclicity constraints.
A new method learns DAG structures from data without false edges.
problem Learning DAG structures from observational data is hard due to combinatorial search space and non-sparse solutions.
method Developed NOTEARS-AL using adaptive Lasso to ensure sparsity and rule out false edges.
result NOTEARS-AL outperforms NOTEARS in synthetic and real-world datasets.
New method estimates causal structure from sparse data.
problem Inferring causal structure from sparse observational data.
method Log-likelihood of sparsely mixed ICA with penalty terms.
result Proposed method outperforms existing methods.
ProDAG uses variational inference to learn DAGs with uncertainty quantification.
problem Statistical and computational challenges in learning a single DAG from data.
method Bayesian variational inference framework with novel distributions.
result ProDAG outperforms state-of-the-art alternatives in accuracy and uncertainty quantification.
New method discovers causal relationships in sparse linear data.
problem Discovering cause-effect relationships in sparse linear data.
method Uses structural matrix to reconstruct data and identify causal structures without independence tests.
result Outperforms existing methods in sparse causal structure recovery.
A fast algorithm for counting Markov equivalent DAGs and designing experiments.
problem Counting Markov equivalent DAGs and designing experiments efficiently.
method LazyIter algorithm for efficient iteration over MECs, utilizing intervention results.
result Significant reduction in time complexity for sparse graphs (O(n)).
New method identifies extreme risk propagation in financial networks.
problem Understanding extreme risk in financial networks.
method Max-linear structural equation model, hard-thresholding, Hamming distance.
result Sparse DAG for extreme risk propagation estimated.
Bayesian networks, with structure given by a directed acyclic graph (DAG), are a popular class of graphical models. However, learning Bayesian networks from discrete or categorical data is particularly challenging, due to the large parameter space and the difficulty in searching for a sparse structure. In this article,…
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…
BCDAG learns causal DAGs from Gaussian data using MCMC.
problem Learning causal DAGs from Gaussian observational data.
method Bayesian approach using MCMC for scalability and convergence diagnostics.
result Efficient scalability with observations and variables.
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes. As our construction shows, the proposed distribution relies on a latent Beta Process controlling both the orders and out…
Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and in practice, because the number of possible DAGs scales superexponentially with the number of nodes. In this paper, we study the problem of learning an optimal DAG from continuous observational data. We cast this problem in the f…
New methods learn DAGs from noisy data, adapting to noise levels.
problem Inferring causal relationships from observational data with noise and confounding.
method Reformulate DAG learning as a continuous optimization problem over adjacency matrices, jointly inferring structure and noise levels.
result Improved robustness to heteroscedasticity and distribution shifts.
Estimates multiple related causal graphs with shared causal order.
problem Discovering multiple related Gaussian DAGs with shared causal order.
method Proposes a l1/l2-regularized MLE for joint estimation of K linear structural equation models. result Joint estimator achieves better sample complexity and consistency in causal order recovery.
Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which …
Learning the directed acyclic graph (DAG) structure of a Bayesian network from observational data is a notoriously difficult problem for which many hardness results are known. In this paper we propose a provably polynomial-time algorithm for learning sparse Gaussian Bayesian networks with equal noise variance --- a cla…
Directed acyclic graphs (DAGs) are commonly used to represent causal relationships among random variables in graphical models. Applications of these models arise in the study of physical, as well as biological systems, where directed edges between nodes represent the influence of components of the system on each other.…
A new differentiable model for sampling DAGs that speeds up optimization.
problem Efficiently sampling and learning DAG structures in continuous optimization.
method DP-DAG model with VI-DP-DAG for DAG learning from data.
result VI-DP-DAG outperforms other methods in DAG structure and causal mechanism learning.
Graphical models are popular statistical tools which are used to represent dependent or causal complex systems. Statistically equivalent causal or directed graphical models are said to belong to a Markov equivalent class. It is of great interest to describe and understand the space of such classes. However, with curren…
New method scores DAGs by identifying unobserved confounding.
problem Unobserved confounding complicates causal discovery.
method Score-based causal discovery algorithm that accounts for unobserved confounding.
result Sparse linear Gaussian DAGs can be recovered from observed data.
Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure learning method, annealing on regularized Cholesky score (ARCS), to search over topological sorts, or permutations of nodes,…
Paper proposes a method to improve DAG structure reconstruction using auxiliary DAGs.
problem Improving DAG structure reconstruction with limited data.
method Introduces structural similarity measures and a transfer learning framework.
result Significant improvement in DAG reconstruction, even with dissimilar auxiliary DAGs.
Paper studies sparsity and DAG constraints for learning linear DAGs.
problem Learning DAGs from data is challenging due to the large search space.
method Formulates structure learning as a constrained optimization problem with soft sparsity and DAG constraints.
result Soft sparsity and DAG constraints lead to an easier optimization problem.
Proposes a hierarchical model for learning discrete Bayesian networks with shrinkage.
problem Learning discrete Bayesian networks with high-order interactions and cell probabilities.
method Hierarchical Dirichlet shrinkage model with Metropolis-adjusted Langevin algorithm for sampling.
result Efficiently learns graph structure and selects between DAGs from sparse count data.
New DAG constraints improve differentiable DAG learning.
problem Recovering DAG structures from observational data is hard due to combinatorial optimization.
method Developed analytic functions to formulate DAG constraints, closed under differentiation, summation, and multiplication.
result Analytic DAG constraints outperform previous methods in various settings.
The paper proposes using low rank assumption to improve causal structure learning in DAGs.
problem Challenges in learning causal structures in high-dimensional, non-sparse DAGs.
method Exploits low rank assumption of DAG adjacency matrix to adapt causal structure learning methods.
result Maximum rank is highly related to hubs, suggesting low rank for scale-free networks.
Differentiable structure learning addresses DAGs with multiple global minimizers.
problem Identify the true DAG from global minimizers of acyclicity-constrained optimization problems.
method Carefully regularize the likelihood to identify the sparsest model in the Markov equivalence class.
result Regularization of the likelihood defines a score that identifies the sparsest model in general models and likelihoods.
New method improves structure learning on sparse graphs.
problem Structure learning on sparse directed acyclic graphs (DAGs).
method Bregman proximal gradient method to address non-convex, high-curvature problem.
result Significantly improved convergence and efficiency.
New method improves DAG learning by using large coefficients for higher-order terms.
problem Recovering DAG structures from observational data is challenging due to combinatorial optimization.
method Proposes truncated matrix power iteration to approximate DAG constraints efficiently.
result Empirically outperforms previous methods by a factor of 3 or more in structural Hamming distance.
Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…
New algorithm speeds up learning of graphical models.
problem Learning graphical models with sparse structure efficiently.
method Vertex-greedy score-based algorithm for learning DAGs.
result Polynomial runtime for learning DAG models.
CASPER improves DAG structure learning by integrating graph structure into score function.
problem Discovering suboptimal DAGs and model vulnerabilities in causal discovery.
method CASPER integrates graph structure into the score function as a new measure in the causal space, enhancing DAG structure learning via adaptive attention to DAG-ness.
result CASPER outperforms state-of-the-art methods in terms of accuracy and robustness.
Learning DAG or Bayesian network models is an important problem in multi-variate causal inference. However, a number of challenges arises in learning large-scale DAG models including model identifiability and computational complexity since the space of directed graphs is huge. In this paper, we address these issues in …
DrBO uses Bayesian optimization to learn DAGs more efficiently.
problem Inaccurate and inefficient DAG learning from observational data.
method Bayesian optimization to find high-scoring DAGs efficiently.
result DrBO finds higher-scoring DAGs more efficiently than existing methods.
This paper proposes MM-DAGs for analyzing traffic congestion, learning multiple DAGs jointly.
problem Analyzing multi-modal traffic data with overlapping and distinct variables.
method Developed MM-DAGs for multi-task, multi-modal DAG learning, using multi-modal regression and CD measure.
result Proved the effectiveness of MM-DAGs in traffic congestion analysis.
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…
Efficiently learns DAG structures without cycles.
problem Learning DAG structures efficiently with constraints.
method Proposes DAG-NoCurl, a two-step algorithm to find and project acyclic graphs.
result Significantly improves efficiency over existing methods, often by more than one order of magnitude.
New framework learns complex AI attitudes from heterogeneous data.
problem Heterogeneous ordinal structure in AI attitudes, poorly captured by existing methods.
method Monotone Gaussian score embedding, BNP complexity discovery, confirmatory fixed-K estimation.
result Reduced holdout MSE by 25.8% over single-graph baseline.
New method aggregates bootstrapped DAGs for causal discovery.
problem Aggregation of bootstrapped DAGs ignores higher-order structures.
method Theoretical framework and new DAG aggregation algorithm.
result Proposed method outperforms state-of-the-art solutions.
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayesian Networks is ordered by DAG Markov model inclusion and it is natural to consider that a good search policy should take this into account.…
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…
Paper proposes DAG-DB for learning discrete DAGs via backpropagation.
problem Learning Directed Acyclic Graphs (DAGs) from data.
method DAG-DB uses Discrete Backpropagation with I-MLE and Straight-Through Estimation.
result DAG-DB learns DAGs effectively using probabilistic sampling and backpropagation.