mlsanityData to 14 Oct 2026

A Survey on Causal Discovery Methods for I.I.D. and Time Series Data

arXiv 2303.15027 ↗First posted 27 Mar 2023cs.AIcited by 35

Not on the map. Why?

Abstract (arXiv)

The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the variables of a system from related observational data with certain assumptions. Over the years, several methods have been developed primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study, we present an extensive discussion on the methods designed to perform causal discovery from both independent and identically distributed (I.I.D.) data and time series data. For this purpose, we first introduce the common terminologies used in causal discovery literature and then provide a comprehensive discussion of the algorithms designed to identify causal relations in different settings. We further discuss some of the benchmark datasets available for evaluating the algorithmic performance, off-the-shelf tools or software packages to perform causal discovery readily, and the common metrics used to evaluate these methods. We also evaluate some widely used causal discovery algorithms on multiple benchmark datasets and compare their performances. Finally, we conclude by discussing the research challenges and the applications of causal discovery algorithms in multiple areas of interest.

Key facts. Cited by 35 papers in the map (data through 14 Oct 2026). Builds on 57 of its 181 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

57 of 181 references.

References · 57

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  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. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  5. 2019Gradient-Based Neural DAG Learningcited by 227
  6. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  7. 2018Neural Autoregressive Flowscited by 208
  8. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
  9. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  10. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  11. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  12. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
38 more
  1. 2019Causal Discovery with Reinforcement Learningcited by 145
  2. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
  3. 2022Causal Machine Learning: A Survey and Open Problemscited by 110
  4. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 100
  5. 2020Causal Autoregressive Flowscited by 99
  6. 2019Masked Gradient-Based Causal Structure Learningcited by 89
  7. 2017Causal Structure Learningcited by 84
  8. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
  9. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  10. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
  11. 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
  12. 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
  13. 2013Learning Gaussian Networkscited by 62
  14. 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
  15. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  16. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  17. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  18. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  19. 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
  20. 2021Interpretable Models for Granger Causality Using Self-explaining Neural Networkscited by 45
  21. 2017Model-Powered Conditional Independence Testcited by 43
  22. 2019Conditional Independence Testing using Generative Adversarial Networkscited by 36
  23. 2013Theory Refinement on Bayesian Networkscited by 28
  24. 2020Neural Additive Vector Autoregression Models for Causal Discovery in Time Seriescited by 28
  25. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  26. 2013Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithmcited by 22
  27. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  28. 2022Causal Structure Discovery between Clusters of Nodes Induced by Latent Factorscited by 18
  29. 2015Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluationscited by 14
  30. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  31. 2022A Meta-Reinforcement Learning Algorithm for Causal Discoverycited by 11
  32. 2019Testing Conditional Independence on Discrete Data using Stochastic Complexitycited by 10
  33. 2021Entropy-based Discovery of Summary Causal Graphs in Time Seriescited by 9
  34. 2017SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guaranteecited by 8
  35. 2019Towards Robust Relational Causal Discoverycited by 8
  36. 2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
  37. 2021FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounderscited by 6
  38. 2021Interactive Causal Structure Discovery in Earth System Sciencescited by 6

50 most cited of 57 All 57 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 12; 2026: 9.

35 papers.

Sort:

By area:

  1. 2024Integrating Large Language Models in Causal Discovery: A Statistical Causal Approachcited by 32
  2. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  3. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  4. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  5. 2023Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecastingcited by 16
  6. 2024Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspectivecited by 16
  7. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  8. 2025Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?cited by 6
  9. 2023Optimizing Data-driven Causal Discovery Using Knowledge-guided Searchcited by 5
  10. 2025Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Predictioncited by 4
  11. 2025Towards Practical Multi-label Causal Discovery in High-Dimensional Event Sequences via One-Shot Graph Aggregationcited by 4
  12. 2025One-Shot Multi-Label Causal Discovery in High-Dimensional Event Sequencescited by 4
23 more
  1. 2025Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noisescited by 3
  2. 2023Causal Temporal Regime Structure Learningcited by 2
  3. 2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 2
  4. 2026Your Autoregressive Model Already Reveals the Causal Graphcited by 2
  5. 2024TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Datacited by 1
  6. 2025Quantum-enhanced causal discovery for a small number of samplescited by 1
  7. 2025Learning What Matters: Causal Time Series Modeling for Arctic Sea Ice Predictioncited by 1
  8. 2025Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shiftcited by 1
  9. 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approachcited by 1
  10. 2024Spatio-Temporal Graphical Counterfactuals: An Overviewcited by 0
  11. 2024Causal Reinforcement Learning for Optimisation of Robot Dynamics in Unknown Environmentscited by 0
  12. 2024MotifDisco: Motif Causal Discovery For Time Series Motifscited by 0
  13. 2024Transformers with Sparse Attention for Granger Causalitycited by 0
  14. 2025SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Inputcited by 0
  15. 2025Nonlinear Causal Discovery through a Sequential Edge Orientation Approachcited by 0
  16. 2025Guide: Generalized-Prior and Data Encoders for DAG Estimationcited by 0
  17. 2026Multi-Agent Causal Reasoning System for Error Pattern Rule Automation in Vehiclescited by 0
  18. 2026Sample Complexity of Causal Identification with Temporal Heterogeneitycited by 0
  19. 2026From Causal Graphs to Markovian States: Multi-Order State Exposure for Deep RLcited by 0
  20. 2026TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Datacited by 0
  21. 2026Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretrainingcited by 0
  22. 2026A Step Towards Inherently Interpretable Causal Machine Learning Models For Decision Supportcited by 0
  23. 2026One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady Statecited by 0