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.
Bayesian method identifies causal DAG structure from non-Gaussian errors.
problem Learning causal structure from non-Gaussian errors in Bayesian networks.
method Bayesian hierarchical model with DAG prior for non-Gaussian errors.
result Posterior DAG selection consistency achieved under mild assumptions.
Efficiently learns linear non-Gaussian DAGs with noisy nodes.
problem Learning DAGs with non-Gaussian noise and diverging number of nodes.
method Proposes a novel method using topological layers for bottom-up reconstruction and consistent parent-child relations.
result Topological layers can be exactly reconstructed and parent-child relations established without faithfulness assumption.
Bayesian method models binary response and covariates for two groups, estimating causal relationships.
problem Estimating causal relationships between binary response and covariates in observational data.
method Gaussian DAG-probit model with MCMC sampling for posterior distribution estimation.
result Validated method on simulated and real datasets, showing value of grouping variable in causality.
Optimal sample complexity for learning Gaussian DAG models established.
problem Learning the structure of Gaussian DAG models from observational data.
method Established minimax optimal sample complexity for two settings: equal variances without ordering knowledge and general linear models with ordering knowledge.
result Optimal sample complexity n≍qlog(d/q) for both settings, matching undirected graphical models under equal variances. Paper proposes a new multi-task causal Gaussian process model for better prediction and uncertainty estimation.
problem Learning causal effects of interventions on different subsets of variables in a DAG.
method DAG-GP model that allows information sharing across interventions and experiments on different variables.
result DAG-GP achieves the best fitting performance and faster optimal intervention selection compared to single-task models.
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.
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.
Develops methods for constructing parameter priors in DAG models.
problem Constructing parameter priors for model choice among DAG models.
method Introduces assumptions and methods for parameter priors construction and marginal likelihood computation.
result The only parameter prior for complete Gaussian DAG models that satisfies assumptions is the normal-Wishart distribution.
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.
Structural learning of directed acyclic graphs (DAGs) or Bayesian networks has been studied extensively under the assumption that data are independent. We propose a new Gaussian DAG model for dependent data which assumes the observations are correlated according to an undirected network. Under this model, we develop a …
Paper learns DAGs with quadratic variance functions efficiently.
problem Learning DAGs with quadratic variance functions.
method Introduces topological layers to reconstruct DAGs hierarchically.
result Efficient algorithm reduces computational cost.
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.
ExDAG solves DAG learning problems with low structural Hamming distance.
problem Learning DAGs with low structural Hamming distance under identifiability assumptions.
method Mixed-integer quadratic programming (MIQP) with branch-and-bound-and-cut algorithm and lazy constraints.
result ExDAG guarantees global convergence and provides a real-time quality assessment.
Deep Gaussian Processes model functions on DAGs with partially observed data.
problem Reconstructing and inferring from partially observed functions on DAGs with noisy measurements.
method Place priors over functions on DAGs, theoretically study prior-collapse behavior, and offer a structured variational approximation.
result Almost-sure lower bounds on the preservation of input distinctions and interpretability of simulator hierarchies.
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.
New proof shows how to identify DAGs with weakly increasing errors.
problem Identifying the true DAG in models with weakly increasing error variances.
method Minimum-trace DAG method and hill climbing algorithm with R2R neighborhood.
result Hill climbing algorithm without strict local optima under weakly increasing error variances.
We study a family of regularized score-based estimators for learning the structure of a directed acyclic graph (DAG) for a multivariate normal distribution from high-dimensional data with p≫n. Our main results establish support recovery guarantees and deviation bounds for a family of penalized least-squares estima…
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…
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.
GES algorithm improves consistency for nonparametric DAG models.
problem Consistent estimation of nonparametric DAG models.
method Greedy equivalence search with new consistency proof for nonparametric families.
result Consistency of GES for general nonparametric DAG models with smooth factorization.
BCD Nets use variational inference to estimate DAGs with uncertainty.
problem Uncertainty in inferring causal graphs from limited data.
method Variational inference framework for Bayesian DAG estimation.
result BCD Nets outperform maximum-likelihood methods in low data regimes.
DAG models with hidden variables present many difficulties that are not present when all nodes are observed. In particular, fully observed DAG models are identified and correspond to well-defined sets ofdistributions, whereas this is not true if nodes are unobserved. Inthis paper we characterize exactly the set of dist…
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.
A new framework estimates causal effects for ordinal variables.
problem Existing causal inference methods fail for ordinal data.
method Presumes a latent Gaussian DAG model with constrained covariance matrix.
result Closed-form function for ordinal causal effects in latent space.
We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model. Our framework consists of two parts: (1) inferring the moralized graph from the support of the inverse covariance matrix; and (2) selecting th…
Paper develops a method to learn causal networks with non-invertible functions.
problem Identifying causal relationships from observational data with non-invertible functional relationships.
method Proposes a test for non-invertible bivariate causal models and develops a method to incorporate this test in structure learning of DAGs.
result Our algorithms outperform existing DAG learning methods in identifying causal graphical structures.
Bayesian method learns causal orderings from heterogeneous data.
problem Learning causal structure from heterogeneous data.
method Order-based Bayesian framework for Gaussian DAG models.
result Causal ordering is identifiable up to two permutations.
Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.
problem Estimating causal directed acyclic graphs with latent confounders.
method Uses higher-order cumulants to identify causal structures among observed and latent variables.
result Validates the proposed algorithm through simulations and real-world data.
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.
Bayesian method recovers causal structure in SEMs with equal error variances.
problem Recovering causal structure in SEMs with equal error variances.
method Bayesian DAG selection method using g-priors and the key property of minimum expected squared errors.
result The method consistently recovers the true graph without additional distributional assumptions.
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.
New algorithm estimates causal effects for non-Gaussian data.
problem Estimating causal effects in non-Gaussian distributions.
method Generalized k-Triangle Faithfulness Assumption and Edge Estimation Algorithm.
result Uniformly consistent estimates of causal effects.
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.
We develop a method to summarize causal models with cycles in cubic time.
problem Cycles in high-dimensional causal models limit applicability of existing methods.
method We relax the acyclicity assumption in LiNG models and develop a low-dimensional DAG summary.
result Our method allows recovery of a low-dimensional DAG from high-dimensional data with cycles.
We identify direct causes of a target variable from observational data without full DAG identifiability.
problem Learning direct causes of a target variable from observational data.
method Developed algorithms under relaxed identifiability assumptions for one environment without interventions.
result Identifiable set of direct causes from observational data under specific assumptions.
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.
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,…
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 method recovers causal DAGs from general environments without strict assumptions.
problem Recovering causal DAGs from real-world data with varying distributions.
method Formalizes desiderata for causal representation learning in general environments, leveraging sufficient change conditions up to third-order derivatives.
result Fully recovers latent DAG and identifies latent variables up to minor indeterminacies under nonparametric mixing.
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 …
New method discovers causal relationships in large-scale data.
problem Discovering causal relationships in large datasets with thousands of variables.
method Factor Directed Acyclic Graphs (f-DAGs) combined with continuous optimization.
result Achieved causal discovery on thousands of variables.
TSLiNGAM improves causal discovery in heavy-tailed data.
problem Identifying causal relationships in data with heavy tails.
method Combines DAGs with structural causal models, leveraging non-Gaussian noise.
result Significantly better performance on heavy-tailed and skewed data.
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.
TSCD is an algorithm for causal discovery using second-order statistics.
problem Causal discovery
method Tensor-based Second-order Causal Discovery (TSCD)
result Identifiable causal order and parameters from logarithmic number of interventions
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.