Score-matching-based Structure Learning for Temporal Data on Networks
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Abstract (arXiv)
Causal discovery is a crucial initial step in establishing causality from empirical data and background knowledge. Numerous algorithms have been developed for this purpose. Among them, the score-matching method has demonstrated superior performance across various evaluation metrics, particularly for the commonly encountered Additive Nonlinear Causal Models. However, current score-matching-based algorithms are primarily designed to analyze independent and identically distributed (i.i.d.) data. More importantly, they suffer from high computational complexity due to the pruning step required for handling dense Directed Acyclic Graphs (DAGs). To enhance the scalability of score matching, we have developed a new parent-finding subroutine for leaf nodes in DAGs, significantly accelerating the most time-consuming part of the process: the pruning step. This improvement results in an efficiency-lifted score matching algorithm, termed Parent Identification-based Causal structure learning for both i.i.d. and temporal data on networKs, or PICK. The new score-matching algorithm extends the scope of existing algorithms and can handle static and temporal data on networks with weak network interference. Our proposed algorithm can efficiently cope with increasingly complex datasets that exhibit spatial and temporal dependencies, commonly encountered in academia and industry. The proposed algorithm can accelerate score-matching-based methods while maintaining high accuracy in real-world applications.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 65 references that are in the map. First posted on arXiv 10 Dec 2024.
Builds on
25 of 65 references.
References · 25
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
- 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
- 2019Gradient-Based Neural DAG Learningcited by 227
- 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
- 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
- 2022Vision GNN: An Image is Worth Graph of Nodescited by 186
- 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
- 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
- 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
- 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
- 2013Learning Gaussian Networkscited by 62
13 more
- 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
- 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
- 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
- 2023Scalable Causal Discovery with Score Matchingcited by 45
- 2023Causal Discovery with Score Matching on Additive Models with Arbitrary Noisecited by 43
- 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 36
- 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
- 2015Estimating the Causal Impact of Recommendation Systems from Observational Datacited by 21
- 2013A Sound and Complete Algorithm for Learning Causal Models from Relational Datacited by 12
- 2024Structure learning of Hamiltonians from real-time evolutioncited by 9
- 2021Hessian Estimation via Stein's Identity in Black-Box Problemscited by 8
- 2022Directed Acyclic Graph Structure Learning from Dynamic Graphscited by 5
- 2022MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Propertiescited by 3
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.