mlsanityData to 7 Oct 2026

Causality from Bottom to Top: A Survey

arXiv 2403.11219 ↗First posted 17 Mar 2024cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems, anomaly detection, robotics, control, sociology, marketing, and advertising. In this paper, we survey its development over the past five decades, shedding light on the differences between causality and other approaches, as well as the preconditions for using it. Furthermore, the paper illustrates how causality interacts with new approaches such as Artificial Intelligence (AI), Generative AI (GAI), Machine and Deep Learning, Reinforcement Learning (RL), and Fuzzy Logic. We study the impact of causality on various fields, its contribution, and its interaction with state-of-the-art approaches. Additionally, the paper exemplifies the trustworthiness and explainability of causality models. We offer several ways to evaluate causality models and discuss future directions.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 69 of its 260 references that are in the map. First posted on arXiv 17 Mar 2024.

Builds on

69 of 260 references.

References · 69

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2017Counterfactual Fairnesscited by 802
  3. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  4. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
  5. 2015Doubly Robust Off-policy Value Evaluation for Reinforcement Learningcited by 337
  6. 2019Causality for Machine Learningcited by 242
  7. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  8. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
  9. 2020A Survey on Causal Inferencecited by 202
  10. 2019Explainable Reinforcement Learning Through a Causal Lenscited by 177
  11. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 177
  12. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
38 more
  1. 2019Feature relevance quantification in explainable AI: A causal problemcited by 164
  2. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  3. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  4. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 150
  5. 2020Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Modelscited by 107
  6. 2016Causal Bandits: Learning Good Interventions via Causal Inferencecited by 100
  7. 2019CXPlain: Causal Explanations for Model Interpretation under Uncertaintycited by 98
  8. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
  9. 2020Causal Interpretability for Machine Learning -- Problems, Methods and Evaluationcited by 82
  10. 2024Robust agents learn causal world modelscited by 81
  11. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  12. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  13. 2018Graphical Models for Processing Missing Datacited by 57
  14. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  15. 2020Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learningcited by 53
  16. 2015A Modification of the Halpern-Pearl Definition of Causalitycited by 47
  17. 2020Counterfactual Fairness with Disentangled Causal Effect Variational Autoencodercited by 45
  18. 2022Causal Inference in Recommender Systems: A Survey and Future Directionscited by 44
  19. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  20. 2022Causal Fairness Analysiscited by 42
  21. 2022Causal Imitation Learning with Unobserved Confounderscited by 40
  22. 2017Budgeted Experiment Design for Causal Structure Learningcited by 38
  23. 2021Tell me why! Explanations support learning relational and causal structurecited by 36
  24. 2023The Inadequacy of Shapley Values for Explainabilitycited by 30
  25. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  26. 2021How Well do Feature Visualizations Support Causal Understanding of CNN Activations?cited by 28
  27. 2020Causality-based Explanation of Classification Outcomescited by 25
  28. 2022Sequential Causal Imitation Learning with Unobserved Confounderscited by 25
  29. 2013A Bayesian Approach to Learning Causal Networkscited by 20
  30. 2020Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI Constraintscited by 20
  31. 2020Artificial Intelligence is stupid and causal reasoning won't fix itcited by 20
  32. 2023Causal Deep Learningcited by 20
  33. 2024Neural Causal Abstractionscited by 20
  34. 2024Counterfactual Image Editingcited by 20
  35. 2013A General Algorithm for Deciding Transportability of Experimental Resultscited by 17
  36. 2022Partial Identification of Treatment Effects with Implicit Generative Modelscited by 17
  37. 2021Causal Multi-Agent Reinforcement Learning: Review and Open Problemscited by 16
  38. 2023Invariant Causal Imitation Learning for Generalizable Policiescited by 15

50 most cited of 69 All 69 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 2.

5 papers.

  1. 2025Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecuritycited by 6
  2. 2025Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generationcited by 4
  3. 2025Estimating Bidirectional Causal Effects with Large Scale Online Kernel Learningcited by 0
  4. 2026Integrating Causal Machine Learning into Clinical Decision Support Systems: Insights from Literature and Practicecited by 0
  5. 2026Logic, Optimization, and Artificial Intelligencecited by 0