mlsanityData to 7 Oct 2026

A Survey on Causal Discovery: Theory and Practice

arXiv 2305.10032 ↗First posted 17 May 2023cs.AIcited by 63

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Abstract (arXiv)

Understanding the laws that govern a phenomenon is the core of scientific progress. This is especially true when the goal is to model the interplay between different aspects in a causal fashion. Indeed, causal inference itself is specifically designed to quantify the underlying relationships that connect a cause to its effect. Causal discovery is a branch of the broader field of causality in which causal graphs are recovered from data (whenever possible), enabling the identification and estimation of causal effects. In this paper, we explore recent advancements in causal discovery in a unified manner, provide a consistent overview of existing algorithms developed under different settings, report useful tools and data, present real-world applications to understand why and how these methods can be fruitfully exploited.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). Builds on 32 of its 128 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

32 of 128 references.

References · 32

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  3. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  4. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  5. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  6. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  7. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  8. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  9. 2020CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learningcited by 109
  10. 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
  11. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  12. 2018Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolutioncited by 66
20 more
  1. 2013Learning Gaussian Networkscited by 62
  2. 2013Causal Discovery from Changescited by 52
  3. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  4. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  5. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  6. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 44
  7. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  8. 2015backShift: Learning causal cyclic graphs from unknown shift interventionscited by 37
  9. 2018Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounderscited by 35
  10. 2013A Discovery Algorithm for Directed Cyclic Graphscited by 33
  11. 2014Distinguishing cause from effect using observational data: methods and benchmarkscited by 33
  12. 2016From Deterministic ODEs to Dynamic Structural Causal Modelscited by 32
  13. 2017Markov Properties for Graphical Models with Cycles and Latent Variablescited by 32
  14. 2020Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cyclescited by 31
  15. 2013Directed Cyclic Graphical Representations of Feedback Modelscited by 27
  16. 2016Ancestral Causal Inferencecited by 24
  17. 2017Fast Causal Inference with Non-Random Missingness by Test-Wise Deletioncited by 22
  18. 2014Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimised Implementations in the bnlearn R Packagecited by 19
  19. 2016An Empirical-Bayes Score for Discrete Bayesian Networkscited by 9
  20. 2021ParaLiNGAM: Parallel Causal Structure Learning for Linear non-Gaussian Acyclic Modelscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 17; 2025: 23; 2026: 18.

63 papers.

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  1. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  2. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  3. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  4. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  5. 2025Causally Reliable Concept Bottleneck Modelscited by 17
  6. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  7. 2026EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environmentscited by 12
  8. 2025LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discoverycited by 7
  9. 2024Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decompositioncited by 6
  10. 2024Large-Scale Targeted Cause Discovery via Learning from Simulated Datacited by 5
  11. 2024Partial Structure Discovery is Sufficient for No-regret Learning in Causal Banditscited by 5
  12. 2024Causal Inference with Latent Variables: Recent Advances and Future Prospectivescited by 4
38 more
  1. 2024Visual Analysis of Multi-outcome Causal Graphscited by 4
  2. 2025SimBank: from Simulation to Solution in Prescriptive Process Monitoringcited by 4
  3. 2025Differentiable Constraint-Based Causal Discoverycited by 4
  4. 2024Detecting and Measuring Confounding Using Causal Mechanism Shiftscited by 3
  5. 2025Adversarial Causal Tuning for Realistic Time-series Generationcited by 3
  6. 2025Revealing Multimodal Causality with Large Language Modelscited by 3
  7. 2024Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementaritycited by 2
  8. 2025A Fast Kernel-based Conditional Independence test with Application to Causal Discoverycited by 2
  9. 2025Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Recordscited by 2
  10. 2025Towards Reasonable Concept Bottleneck Modelscited by 2
  11. 2025Causal Discovery on Higher-Order Interactionscited by 2
  12. 2026Test-Time Learning of Causal Structure from Interventional Datacited by 2
  13. 2024Causal Layering via Conditional Entropycited by 1
  14. 2024A review of feature selection strategies utilizing graph data structures and knowledge graphscited by 1
  15. 2024The Importance of Causality in Decision Making: A Perspective on Recommender Systemscited by 1
  16. 2024Unified Causality Analysis Based on the Degrees of Freedomcited by 1
  17. 2024Causal Order Discovery based on Monotonic SCMscited by 1
  18. 2025How Well Does Your Tabular Generator Learn the Structure of Tabular Data?cited by 1
  19. 2025Causal-Paced Deep Reinforcement Learningcited by 1
  20. 2025Interpretable Clustering with Adaptive Heterogeneous Causal Structure Learning in Mixed Observational Datacited by 1
  21. 2026Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approachcited by 1
  22. 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approachcited by 1
  23. 2026Order-based Rehearsal Learningcited by 1
  24. 2023Learning Linear Gaussian Polytree Models with Interventionscited by 0
  25. 2023Towards a Transportable Causal Network Model Based on Observational Healthcare Datacited by 0
  26. 2023Shapley-PC: Constraint-based Causal Structure Learning with a Shapley Inspired Frameworkcited by 0
  27. 2024Inference of Causal Networks using a Topological Thresholdcited by 0
  28. 2024CURATE: Scaling-up Differentially Private Causal Graph Discoverycited by 0
  29. 2025How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errorscited by 0
  30. 2025Symmetric observations without symmetric causal explanationscited by 0
  31. 2025Graphical Models for Decision-Making: Integrating Causality and Game Theorycited by 0
  32. 2025Causal Beam Selection for Reliable Initial Access in AI-driven Beam Managementcited by 0
  33. 2025Towards Privacy-Aware Bayesian Networks: A Credal Approachcited by 0
  34. 2025Guide: Generalized-Prior and Data Encoders for DAG Estimationcited by 0
  35. 2025Robust Optimization in Causal Models and G-Causal Normalizing Flowscited by 0
  36. 2025Combining SHAP and Causal Analysis for Interpretable Fault Detection in Industrial Processescited by 0
  37. 2025Graph Distance Based on Cause-Effect Estimands with Latentscited by 0
  38. 2025Stress-Testing Causal Claims via Cardinality Repairscited by 0

50 most cited of 63 All 63 (JSON)