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.
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.
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…
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.
D-VAE generates valid DAGs for neural architecture search and Bayesian network learning.
problem Generating valid DAGs for machine learning models.
method Proposes a novel DAG variational autoencoder (D-VAE) using graph neural networks and asynchronous message passing.
result Demonstrates the effectiveness of D-VAE through neural architecture search and Bayesian network structure learning.
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.
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.
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…
DAG-WGAN learns causal structures using Wasserstein distance.
problem Learning causal structures from data with combinatorial challenges.
method Combines Wasserstein distance, auto-encoder, and acyclicity constraint.
result Demonstrates good performance compared to state-of-the-art models.
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.
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.
Proposes DAG-GNN for learning DAGs from samples using graph neural networks.
problem Learning DAGs from samples is a combinatorial problem with an intractable search space.
method Proposes a deep generative model with a novel graph neural network architecture to learn DAGs.
result Demonstrates improved accuracy in learning DAGs for nonlinearly generated samples and reasonable accuracy for discrete variables.
Proposes supervised method for whole DAG causal structure learning.
problem Learning causal directions from data, especially for whole DAG structure.
method Supervised learning approach using permutation equivariant models.
result Promising results compared to previous approaches on synthetic and real data.
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.
New method learns DAGs from correlated network data.
problem Learning DAGs from dependent network data.
method Jointly estimates DAG structure and correlations using penalized likelihood.
result Method achieves higher accuracy in structure learning.
Counts and samples DAGs equivalent to a ground truth DAG.
problem Identifying the number and structure of DAGs equivalent to a ground truth DAG.
method Clique tree representation of chordal graphs for counting and sampling.
result Polynomial time algorithm for counting and sampling in bounded degree graphs.
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.
ICP models flexible DAG structures using Bayesian nonparametrics.
problem Learning the structure of complex neural networks.
method Bayesian nonparametric prior on DAGs and orders controlled by a latent Beta Process.
result ICP supports every possible DAG structure.
Discover causal structure from mixtures of DAGs using latent variable algorithms.
problem Discover causal structure from distributions arising from mixtures of DAGs.
method Causal structure discovery algorithms such as FCI for latent variables.
result Recover a 'union' of the component DAGs and identify varying conditional distributions.
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.
The paper develops personalized DAG models for web user behavior.
problem Understanding user behavior transitions between websites with user heterogeneity and network dependency.
method Personalized Binomial DAG models with network-structured covariates, embedding network structure into a dimension-reduced covariate, learning node neighborhoods, and exploring variance-mean relation.
result The proposed algorithm outperforms state-of-the-art competitors in heterogeneous 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 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.
A new method for learning DAG structure without combinatorial constraints.
problem Estimating the structure of directed acyclic graphs (DAGs).
method Formulate structure learning as a continuous optimization problem over real matrices.
result The method outperforms existing approaches without structural assumptions.
FedDAG learns DAG structures from decentralized data.
problem Learning DAG structures from private decentralized data.
method Gradient-based framework with two-level structures.
result FedDAG learns DAG structure without direct data sharing.
Framework for learning sparse DAGs from data.
problem Learning sparse directed acyclic graphs (DAGs) from data.
method Algebraic characterization of DAGs extended to nonparametric SEM, continuous optimization problem.
result General framework applicable to various nonparametric and semiparametric models.
We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior …
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.
DAPDAG learns DAG structure to adapt predictions across domains.
problem Predicting in a new domain without labeled data.
method Auto-encoder with DAG reconstruction as auxiliary task.
result Reconstructing DAG benefits approximate inference and prediction.
Paper solves DAG learning from continuous data using integer programming.
problem Learning optimal DAGs from continuous observational data.
method Formulated as mixed-integer quadratic optimization (MIQO) model with penalties and regularizations.
result LN formulation outperforms existing methods in computational time and optimality.
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 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.
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.…
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.
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.
A new method infers causal gene regulatory networks from parallel CRISPR interventions and transcriptomic data.
problem Learning causal gene regulatory networks from observational data is complicated by lack of identifiability and a combinatorial solution space.
method A continuous optimization framework that leverages observational and interventional data to infer a single causal structure, assuming a linear Structural Equation Model (SEM).
result A provably consistent estimator of the true DAG under mild assumptions.
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…
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.
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…
Disk Embeddings tackle embedding DAGs with exponential growth.
problem Embedding DAGs with exponentially increasing ancestors and descendants.
method Disk Embeddings framework for quasi-metric spaces, including Hyperbolic Disk Embeddings.
result Disk Embeddings outperform existing methods in complex DAGs.
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.
Variational Causal Networks approximate Bayesian inference over causal structures.
problem Quantifying uncertainty in causal structure inference from finite data.
method Parametric variational family over DAGs, using Evidence Lower Bound (ELBO) for tractable learning.
result Approximation of the true posterior over DAGs is demonstrated to be good.
New insights into convergence of optimization methods for DAG structure learning.
problem Unclear convergence properties of optimization methods for structure learning.
method Examined the convergence of augmented Lagrangian method (ALM) and quadratic penalty method (QPM) for structure learning.
result Standard convergence result of ALM does not hold in various cases, and QPM is prone to ill-conditioning.
New method tests DAGs without assuming linear or independent data.
problem Testing DAGs with nonlinear and time-dependent data.
method Structural, supervised and generative adversarial learning.
result Asymptotic guarantees for the test, allowing diverging data dimensions.
Simulated DAGs can mislead structure learning algorithms due to variance patterns.
problem Structure learning algorithms can be misled by variance patterns in simulated DAG models.
method Introduced varsortability as a measure of agreement between marginal variance order and causal order.
result Performance of structure learning algorithms can be explained by high varsortability, but this does not generalize to real-world data.
DAG-LSTM improves DA classification in group chats.
problem DA classification in multi-party conversations.
method Directed-Acyclic-Graph LSTM (DAG-LSTM) exploiting turn-taking structure.
result DAG-LSTM outperforms existing methods by 0.8% in accuracy and 1.2% in macro-F1 score.
PIVID infers DAG structures from data using variational inference and permutations.
problem Estimating the structure of Bayesian networks from observational data.
method PIVID uses variational inference and continuous relaxations of discrete distributions to infer a distribution over permutations and DAGs.
result PIVID outperforms deterministic and Bayesian approaches in estimating DAG structures from data.
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.