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Gradient-Based Neural DAG Learning

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arxiv 1906.02226 v2 pith:C2NPTRQ7 submitted 2019-06-05 cs.LG stat.ML

Sébastien Lachapelle , Philippe Brouillard , Tristan Deleu , Simon Lacoste-Julien This is my paper
classification cs.LGstat.ML
keywords methodscontinuousdataexistinggreedylearningmethodneural
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We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while avoiding the combinatorial nature of the problem. In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods. On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks, while being competitive with existing greedy search methods on important metrics for causal inference.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 15 citations worldwide. Full citation record

  1. SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discovery

    cs.LG 2026-08 reject novelty 6.0 of 10

    SVI-DAG couples normalizing flows over edge logits with stein variational gradient descent on node orderings to learn multimodal Bayesian posteriors over DAGs.

  2. polyDAG: Polynomial Acyclicity Constraints for Efficient Continuous Causal Discovery in Visual Semantic Graphs

    cs.CV 2026-06 conditional novelty 6.0 of 10

    polyDAG replaces the matrix-exponential acyclicity constraint with a finite polynomial trace constraint proven to be zero exactly on acyclic graphs, plus a geometric-series implementation, yielding faster runtime and ...

  3. Toward Temporal Causal Representation Learning with Tensor Decomposition

    cs.LG 2025-07 conditional novelty 6.0 of 10

    CaRTeD jointly learns latent phenotypes and the temporal causal network among them from irregular tensors like EHR data, with a convergence guarantee under strong assumptions.

  4. CauScale: Neural Causal Discovery at Scale

    cs.LG 2026-02 conditional novelty 5.0 of 10

    CauScale uses a two-stream neural architecture with a sample-reduction unit and tied attention weights to scale amortized causal discovery to 1000-node graphs.

  5. From Images to Insights: Explainable Biodiversity Monitoring with Plain Language Habitat Explanations

    cs.CV 2025-06 conditional novelty 4.0 of 10

    An image-to-text pipeline that combines species recognition, occurrence data, and causal inference to produce plain-language habitat preference explanations, demonstrated on one bee and one flower species.

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