mlsanityData to 13 Oct 2026

A Survey on Causal Inference

arXiv 2002.02770 ↗First posted 5 Feb 2020stat.ME cs.AI cs.LGcited by 202

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Abstract (arXiv)

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well known causal inference framework. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

Key facts. Cited by 202 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 Feb 2020.

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Citing papers per year: 2021: 11; 2022: 37; 2023: 47; 2024: 57; 2025: 34; 2026: 16.

202 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 210
  2. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 81
  3. 2023Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Modelscited by 59
  4. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  5. 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 56
  6. 2022A Causal Lens for Controllable Text Generationcited by 45
  7. 2023A Survey on Causal Reinforcement Learningcited by 44
  8. 2021Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Datacited by 42
  9. 2021Factual Consistency Evaluation for Text Summarization via Counterfactual Estimationcited by 37
  10. 2024Causal Inference with Large Language Model: A Surveycited by 31
  11. 2025Token Activation Map to Visually Explain Multimodal LLMscited by 31
  12. 2024Causality for Large Language Modelscited by 24
38 more
  1. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  2. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  3. 2022Fair ranking: a critical review, challenges, and future directionscited by 21
  4. 2024ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Frameworkcited by 20
  5. 2021Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decompositioncited by 19
  6. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
  7. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  8. 2024A Causal Explainable Guardrails for Large Language Modelscited by 18
  9. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  10. 2022LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Modelscited by 16
  11. 2024Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Modelscited by 16
  12. 2024From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditingcited by 15
  13. 2021Improving Weakly-supervised Object Localization via Causal Interventioncited by 14
  14. 2023Causal Effect Estimation: Recent Advances, Challenges, and Opportunitiescited by 14
  15. 2023Knowledge Graph Completion with Counterfactual Augmentationcited by 13
  16. 2023Continual Causal Inference with Incremental Observational Datacited by 13
  17. 2022Applications of statistical causal inference in software engineeringcited by 12
  18. 2025Disentangled Knowledge Tracing for Alleviating Cognitive Biascited by 12
  19. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  20. 2023Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimationcited by 11
  21. 2023Generalization bound for estimating causal effects from observational network datacited by 11
  22. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  23. 2022Instrumental Variables in Causal Inference and Machine Learning: A Surveycited by 10
  24. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  25. 2022Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Datacited by 9
  26. 2023Discovering and Explaining the Non-Causality of Deep Learning in SAR ATRcited by 9
  27. 2023Covariate balancing using the integral probability metric for causal inferencecited by 9
  28. 2023Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Predictioncited by 9
  29. 2023LLM4Causal: Democratized Causal Tools for Everyone via Large Language Modelcited by 9
  30. 2026Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structurecited by 9
  31. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  32. 2022Causal Inference via Style Transfer for Out-of-distribution Generalisationcited by 8
  33. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  34. 2024Interpreting Low-level Vision Models with Causal Effect Mapscited by 8
  35. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  36. 2022FAST: Improving Controllability for Text Generation with Feedback Aware Self-Trainingcited by 7
  37. 2023Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confoundingcited by 7
  38. 2023Estimating Treatment Effects Under Heterogeneous Interferencecited by 7

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