mlsanityData to 7 Oct 2026

Towards Automated Causal Discovery: a case study on 5G telecommunication data

arXiv 2402.14481 ↗First posted 22 Feb 2024cs.LG stat.MEcited by 1

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Abstract (arXiv)

We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods. AutoCD's goal is to deliver all causal information that an expert human analyst would and answer a user's causal queries. We describe the architecture of such a platform, and illustrate its performance on synthetic data sets. As a case study, we apply it on temporal telecommunication data. The system is general and can be applied to a plethora of causal discovery problems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 22 Feb 2024.

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17 of 56 references.

References · 17

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  3. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  4. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  5. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
  6. 2023Causal-learn: Causal Discovery in Pythoncited by 108
  7. 2020DoWhy: An End-to-End Library for Causal Inferencecited by 92
  8. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
  9. 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 75
  10. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  11. 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
  12. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
5 more
  1. 2017Forward-Backward Selection with Early Droppingcited by 18
  2. 2017Bootstrapping the Out-of-sample Predictions for Efficient and Accurate Cross-Validationcited by 12
  3. 2016Feature Selection with the R Package MXM: Discovering Statistically-Equivalent Feature Subsetscited by 10
  4. 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
  5. 2023OpportunityFinder: A Framework for Automated Causal Inferencecited by 2

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Citing papers per year: 2025: 1.

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  1. 2025Adversarial Causal Tuning for Realistic Time-series Generationcited by 3

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  3. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Framework
  4. 2026CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
  5. 2024Large Language Models for Constrained-Based Causal Discovery
  6. 2023Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
  7. 2024LLM-initialized Differentiable Causal Discovery
  8. 2025MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning