mlsanityData to 7 Oct 2026

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications

arXiv 2412.01953 ↗First posted 2 Dec 2024cs.LG stat.MEcited by 17

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Abstract (arXiv)

Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 100 of its 228 references that are in the map. First posted on arXiv 2 Dec 2024.

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100 of 228 references.

References · 100

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38 more
  1. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
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Citing papers per year: 2025: 7; 2026: 10.

17 papers.

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  1. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 7
  2. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  3. 2025Embracing Discrete Search: A Reasonable Approach to Causal Structure Learningcited by 4
  4. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 1
  5. 2025How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errorscited by 0
  6. 2025Linear Causal Discovery with Interventional Constraintscited by 0
  7. 2025Causal-driven attribution (CDA): Estimating channel influence without user-level datacited by 0
  8. 2026Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcarecited by 0
  9. 2026DAGverse: Building Document-Grounded Semantic DAGs from Scientific Paperscited by 0
  10. 2026Toward Artificial Intelligence Enabled Earth System Couplingcited by 0
  11. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  12. 2026Stable Causal Discovery via Directed Acyclic Graph Aggregationcited by 0
5 more
  1. 2026The Good, the Bad, and the Ugly of Markov Boundary for Tabular Predictioncited by 0
  2. 2026EML-CD: Causal Mechanism Recovery via EML Symbolic Trees in Structure Learningcited by 0
  3. 2026CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discoverycited by 0
  4. 2026AutoCause: A Python framework that automates expert decisions in environmental time-series causal discoverycited by 0
  5. 2026GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Auditscited by 0