mlsanityData to 14 Oct 2026

Open problems in causal structure learning: A case study of COVID-19 in the UK

arXiv 2305.03859 ↗First posted 5 May 2023cs.LG cs.AIcited by 7

Not on the map. Why?

Abstract (arXiv)

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation praovided by these algorithms enables transparency and explainability, which is necessary for decision making in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models, data sets, and source code publicly available online.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 26 of its 116 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

26 of 116 references.

References · 26

  1. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  4. 2019Learning Sparse Nonparametric DAGscited by 207
  5. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
  6. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  7. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  8. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  9. 2019Masked Gradient-Based Causal Structure Learningcited by 89
  10. 2021A survey of Bayesian Network structure learningcited by 87
  11. 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
  12. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
14 more
  1. 2020Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy datacited by 29
  2. 2018Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergencescited by 21
  3. 2021Bayesian structure learning and sampling of Bayesian networks with the R package BiDAGcited by 17
  4. 2013Properties of Bayesian Belief Network Learning Algorithmscited by 13
  5. 2021Greedy structure learning from data that contain systematic missing valuescited by 13
  6. 2020Causal Modeling of Twitter Activity During COVID-19cited by 8
  7. 2020Bayesian network structure learning with causal effects in the presence of latent variablescited by 8
  8. 2020Learning Bayesian Networks that enable full propagation of evidencecited by 7
  9. 2021Effective and efficient structure learning with pruning and model averaging strategiescited by 7
  10. 2020Improving Bayesian Network Structure Learning in the Presence of Measurement Errorcited by 6
  11. 2019Online Causal Structure Learning in the Presence of Latent Variablescited by 4
  12. 2020A Constraint-Based Algorithm for the Structural Learning of Continuous-Time Bayesian Networkscited by 4
  13. 2022Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulationcited by 4
  14. 2022Discovery and density estimation of latent confounders in Bayesian networks with evidence lower boundcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 2; 2026: 1.

7 papers.

  1. 2024Investigating potential causes of Sepsis with Bayesian network structure learningcited by 4
  2. 2024Using GPT-4 to guide causal machine learningcited by 3
  3. 2025Stable Structure Learning with HC-Stable and Tabu-Stable Algorithmscited by 2
  4. 2023Tuning structure learning algorithms with out-of-sample and resampling strategiescited by 1
  5. 2024Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetescited by 1
  6. 2025Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Datacited by 1
  7. 2026Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policiescited by 1