mlsanityData to 7 Oct 2026

Causal Machine Learning for Healthcare and Precision Medicine

arXiv 2205.11402 ↗First posted 23 May 2022cs.LG cs.AIcited by 68

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Abstract (arXiv)

Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a complete toolset for investigating how a system would react to an intervention (e.g. outcome given a treatment). Quantifying effects of interventions allows actionable decisions to be made whilst maintaining robustness in the presence of confounders. Here, we explore how causal inference can be incorporated into different aspects of clinical decision support (CDS) systems by using recent advances in machine learning. Throughout this paper, we use Alzheimer's disease (AD) to create examples for illustrating how CML can be advantageous in clinical scenarios. Furthermore, we discuss important challenges present in healthcare applications such as processing high-dimensional and unstructured data, generalisation to out-of-distribution samples, and temporal relationships, that despite the great effort from the research community remain to be solved. Finally, we review lines of research within causal representation learning, causal discovery and causal reasoning which offer the potential towards addressing the aforementioned challenges.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). Builds on 36 of its 120 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

36 of 120 references.

References · 36

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  11. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  12. 2021Towards Causal Representation Learningcited by 207
24 more
  1. 2019Causality matters in medical imagingcited by 193
  2. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  3. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  4. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 154
  5. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  6. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  7. 2022Deep End-to-end Causal Inferencecited by 93
  8. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  9. 2020Causal Interpretability for Machine Learning -- Problems, Methods and Evaluationcited by 82
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  14. 2021Estimating Average Treatment Effects via Orthogonal Regularizationcited by 25
  15. 2018Generalization in anti-causal learningcited by 23
  16. 2021A Structural Causal Model for MR Images of Multiple Sclerosiscited by 21
  17. 2020Review of The Book of Why: The New Science of Cause and Effectcited by 19
  18. 2021Estimating Categorical Counterfactuals via Deep Twin Networkscited by 18
  19. 2020Multi-task Causal Learning with Gaussian Processescited by 14
  20. 2021Harmonization with Flow-based Causal Inferencecited by 14
  21. 2018Disease-Atlas: Navigating Disease Trajectories with Deep Learningcited by 8
  22. 2021A tutorial on individualized treatment effect prediction from randomized trials with a binary endpointcited by 8
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  24. 2022Adversarial Counterfactual Augmentation: Application in Alzheimer's Disease Classificationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 13; 2024: 14; 2025: 23; 2026: 12.

68 papers.

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  1. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 64
  2. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  3. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  4. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  5. 2024Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Modelscited by 17
  6. 2023Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modelingcited by 15
  7. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  8. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  9. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  10. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  11. 2025Diffusion Counterfactual Generation with Semantic Abductioncited by 8
  12. 2024Causal Effect Identification in LiNGAM Models with Latent Confounderscited by 7
38 more
  1. 2022Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processescited by 6
  2. 2024SimSUM: Simulated Benchmark with Structured and Unstructured Medical Recordscited by 6
  3. 2023Optimizing Data-driven Causal Discovery Using Knowledge-guided Searchcited by 5
  4. 2023Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Modelscited by 5
  5. 2024Causality from Bottom to Top: A Surveycited by 5
  6. 2022Innovations in Integrating Machine Learning and Agent-Based Modeling of Biomedical Systemscited by 4
  7. 2024Visual Analysis of Multi-outcome Causal Graphscited by 4
  8. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  9. 2023Diffusion Model in Causal Inference with Unmeasured Confounderscited by 3
  10. 2024Measuring Variable Importance in Heterogeneous Treatment Effects with Confidencecited by 3
  11. 2024MACAW: A Causal Generative Model for Medical Imagingcited by 3
  12. 2026The Patient is not a Moving Document: A World Model Training Paradigm for Longitudinal EHRcited by 3
  13. 2026Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translationcited by 3
  14. 2023Causal Intersectionality and Dual Form of Gradient Descent for Multimodal Analysis: a Case Study on Hateful Memescited by 2
  15. 2024Interpretation of Intracardiac Electrograms Through Textual Representationscited by 2
  16. 2024CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarningcited by 2
  17. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  18. 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulantscited by 2
  19. 2025CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networkscited by 2
  20. 2025Probabilistic Modelling is Sufficient for Causal Inferencecited by 2
  21. 2026A Versatile AI Agent for Rare Disease Diagnosis and Risk Gene Prioritizationcited by 2
  22. 2023Pretrained Vision Models for Predicting High-Risk Breast Cancer Stagecited by 1
  23. 2023Relation-First Modeling Paradigm for Causal Representation Learning toward the Development of AGIcited by 1
  24. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  25. 2024Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Datacited by 1
  26. 2024Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEscited by 1
  27. 2025"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning"cited by 1
  28. 2025Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Datacited by 1
  29. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  30. 2025A Distributionally Robust Framework for Nuisance in Causal Effect Estimationcited by 1
  31. 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 1
  32. 2025Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventionscited by 1
  33. 2025Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Datacited by 1
  34. 2025Towards Causal Representation Learning with Observable Sources as Auxiliariescited by 1
  35. 2025Causal Graph Neural Networks for Healthcarecited by 1
  36. 2026Causal Neighbourhood Learning for Invariant Graph Representationscited by 1
  37. 2022Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Spacecited by 0
  38. 2024Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approachcited by 0

50 most cited of 68 All 68 (JSON)

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  1. 2025REACT-LLM: A Benchmark for Evaluating LLM Integration with Causal Features in Clinical Prognostic Tasks
  2. 2025Visual Analytics for Causal Reasoning from Real-World Health Data ↗
  3. 2026Causal-Enhanced AI Agents for Medical Research Screening
  4. 2025Cross-modal Causal Intervention for Alzheimer's Disease Prediction
  5. 2025Accelerating Causal Network Discovery of Alzheimer Disease Biomarkers via Scientific Literature-based Retrieval Augmented Generation
  6. 2025Causal Reinforcement Learning based Agent-Patient Interaction with Clinical Domain Knowledge
  7. 2025Beyond Black-Box AI: Interpretable Hybrid Systems for Dementia Care
  8. 2025CARE: Turning LLMs Into Causal Reasoning Expert