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Differentiable and Transportable Structure Learning

arXiv 2206.06354 ↗First posted 13 Jun 2022cs.LG stat.MLcited by 0

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Abstract (arXiv)

Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure. However, compute required to infer these structures is typically super-exponential in the number of variables, as inference requires a sweep of a combinatorially large space of potential structures. That is, until recent advances made it possible to search this space using a differentiable metric, drastically reducing search time. While this technique -- named NOTEARS -- is widely considered a seminal work in DAG-discovery, it concedes an important property in favour of differentiability: transportability. To be transportable, the structures discovered on one dataset must apply to another dataset from the same domain. We introduce D-Struct which recovers transportability in the discovered structures through a novel architecture and loss function while remaining fully differentiable. Because D-Struct remains differentiable, our method can be easily adopted in existing differentiable architectures, as was previously done with NOTEARS. In our experiments, we empirically validate D-Struct with respect to edge accuracy and structural Hamming distance in a variety of settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 94 references that are in the map. First posted on arXiv 13 Jun 2022.

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References · 27

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
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  12. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
15 more
  1. 2013Causal Networks: Semantics and Expressivenesscited by 67
  2. 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
  3. 2019Machine Learning Methods Economists Should Know Aboutcited by 64
  4. 2013Learning Gaussian Networkscited by 62
  5. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
  6. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  7. 2021MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanismscited by 51
  8. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
  9. 2013Strong Completeness and Faithfulness in Bayesian Networkscited by 45
  10. 2020CASTLE: Regularization via Auxiliary Causal Graph Discoverycited by 41
  11. 2020On the Convergence of Continuous Constrained Optimization for Structure Learningcited by 38
  12. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  13. 2013On the Logic of Causal Modelscited by 19
  14. 2021Multi-task Learning of Order-Consistent Causal Graphscited by 10
  15. 2013Perfect Tree-Like Markovian Distributionscited by 1

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