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.
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.
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.
A new method learns DAGs from data using permutation optimization.
problem Discovering latent DAGs from observational data.
method Optimizes over the Permutahedron to learn topological orderings and edges.
result Our method optimizes exact DAGs, is modular, and performs well on real-world data.
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 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.
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.
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.
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…
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. 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.
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.
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.
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.
Proposes a method to estimate causal effects over a range of DAGs, addressing uncertainty in prior knowledge.
problem Uncertainty in prior knowledge of causal relationships between variables.
method Gradient-based optimization method providing bounds for causal queries over a collection of causal graphs.
result Bounds achieve good coverage and sharpness for causal queries in various settings.
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.
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…
ALIAS uses RL to learn DAGs without acyclicity constraints.
problem Efficiently learning DAGs from observational data without acyclicity constraints.
method ALIAS employs RL to generate DAGs in a single step with optimal complexity, bypassing acyclicity constraints.
result ALIAS outperforms state-of-the-art methods in causal discovery.
DAGMA learns DAGs faster and more accurately using log-determinant acyclicity.
problem Learning directed acyclic graphs from data efficiently and accurately.
method DAGMA uses M-matrices and log-determinant acyclicity to optimize DAG learning.
result DAGMA achieves faster and more accurate DAG learning compared to existing methods.
Paper proves global optimality of a simple optimization scheme for learning DAG models.
problem Learning acyclic directed graphical models from data.
method Path-following optimization scheme for bivariate setting.
result Simple optimization scheme globally converges to global minimum.
Optimal sample complexity for learning DDAGs from noisy data.
problem Learning interactions in linear dynamical systems over DAGs.
method Proposed a metric and algorithm based on PSD matrix for reconstruction.
result Optimal sample complexity n=Θ(qlog(p/q)) for learning DDAGs. 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.
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.
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…
Paper proposes an algorithm to learn DAGs with indirect dependencies.
problem Learning DAGs misses indirect dependencies in local variables.
method Two-phase algorithm using high-order HSIC for local optimization.
result OT algorithm outperforms existing methods in structure estimation.
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 …
ZICO learns DAGs from zero-inflated count data efficiently.
problem Learning network structures from zero-inflated count data.
method ZICO uses node-wise likelihoods with canonical links and a differentiable surrogate constraint for acyclicity.
result ZICO achieves superior performance and faster runtimes on simulated data.
This paper tackles causal interactions in mixtures of DAGs using interventions.
problem Learning causal interactions among variables governed by a mixture of causal systems.
method Establishes necessary and sufficient conditions for intervention size, designs an adaptive algorithm.
result Identifies true edges in a mixture of DAGs using optimal or near-optimal interventions.
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.
Paper proposes a fast algorithm to recover causal DAGs with latent variables.
problem Discovering causal relationships in the presence of latent variables.
method Cholesky factorization of covariance matrix with optimization for latent variables.
result The algorithm significantly outperforms previous methods in synthetic and real-world datasets.
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.
Neural Spacetimes learn DAGs by embedding nodes in a spacetime manifold.
problem Learning representations of weighted directed acyclic graphs (DAGs).
method Trainable deep learning-based geometries (Neural Spacetimes) that encode both edge weights and causality.
result Universal embedding theorem for DAGs with sub-cubic parameters and low distortion.
Current state of the art methods in Domain Adaptation follow adversarial approaches, making training a challenge. Existing non-adversarial methods learn mappings between the source and target domains, to achieve reasonable performance. However, even these methods do not focus on a key aspect: maintaining performance on…
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…
Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes. A recent breakthrough formulates the problem as a continuous optimization with a structural constraint that e…
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.
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 optimizes DAGs by swapping node pairs to avoid cycles.
problem Optimizing DAGs with non-convex constraints to avoid cycles.
method Bi-level algorithm with iterative node swaps and off-the-shelf solvers.
result Guaranteed to find local minima or KKT points with lower scores.
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 …
Develops a method to efficiently learn causal DAGs using directed clique trees.
problem Efficiently learning causal DAGs in the presence of large cliques.
method Decomposes DAGs into independently orientable components using directed clique trees and designs a two-phase intervention algorithm.
result Proves that the number of single-node interventions necessary to orient any DAG in an EC is at least the sum of half the size of the largest cliques in each chain component of the essential graph.
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.
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,…
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales superexponentially with the number of nodes. Existing approaches rely on various local heuristics for enforcing the acyclicity constraint. In th…
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.
LOCAL learns dynamic causal structures from time series data efficiently.
problem Challenges in discovering DAG from time series data due to dynamic nature and nonlinear interactions.
method LOCAL proposes a quasi-maximum likelihood-based score function and adaptive modules ACML and DGPL.
result LOCAL significantly outperforms existing methods in dynamic causal discovery.
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.…
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.
COSMO learns DAG structure without acyclicity constraints.
problem Learning DAG structure from data efficiently and without constraints.
method Differentiable approximation of smooth orientation matrix.
result COSMO converges to acyclic solutions without evaluating acyclicity.