mlsanityData to 7 Oct 2026

Causal machine learning for predicting treatment outcomes

arXiv 2410.08770 ↗First posted 11 Oct 2024cs.LG stat.AP stat.MLcited by 73

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Abstract (arXiv)

Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). Builds on 44 of its 102 references that are in the map. First posted on arXiv 11 Oct 2024.

Builds on

44 of 102 references.

References · 44

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  2. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  3. 2017Quasi-Oracle Estimation of Heterogeneous Treatment Effectscited by 328
  4. 2016Generalized Random Forestscited by 309
  5. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 285
  6. 2015Recursive Partitioning for Heterogeneous Causal Effectscited by 254
  7. 2017Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processescited by 156
  8. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 154
  9. 2019Orthogonal Statistical Learningcited by 150
  10. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
  11. 2022Causal Transformer for Estimating Counterfactual Outcomescited by 121
  12. 2022Causal Machine Learning: A Survey and Open Problemscited by 107
32 more
  1. 2019Learning Counterfactual Representations for Estimating Individual Dose-Response Curvescited by 90
  2. 2020Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networkscited by 78
  3. 2018Removing Hidden Confounding by Experimental Groundingcited by 64
  4. 2015Nonparametric methods for doubly robust estimation of continuous treatment effectscited by 62
  5. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  6. 2021VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatmentscited by 59
  7. 2022Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equationscited by 59
  8. 2020Identifying Causal-Effect Inference Failure with Uncertainty-Aware Modelscited by 57
  9. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  10. 2020Causal inference methods for combining randomized trials and observational studies: a reviewcited by 50
  11. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  12. 2022Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effectscited by 32
  13. 2019Machine Learning Estimation of Heterogeneous Treatment Effects with Instrumentscited by 29
  14. 2022Estimating average causal effects from patient trajectoriescited by 29
  15. 2023In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimationcited by 29
  16. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  17. 2023Bayesian Neural Controlled Differential Equations for Treatment Effect Estimationcited by 26
  18. 2023Sharp Bounds for Generalized Causal Sensitivity Analysiscited by 24
  19. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Pythoncited by 23
  20. 2023Conformal Meta-learners for Predictive Inference of Individual Treatment Effectscited by 23
  21. 2020Estimating Individual Treatment Effects with Time-Varying Confounderscited by 21
  22. 2022Normalizing Flows for Interventional Density Estimationcited by 21
  23. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  24. 2022Estimating individual treatment effects under unobserved confounding using binary instrumentscited by 17
  25. 2021Generalizing Off-Policy Learning under Sample Selection Biascited by 16
  26. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  27. 2023Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Timecited by 14
  28. 2020G-Net: A Deep Learning Approach to G-computation for Counterfactual Outcome Prediction Under Dynamic Treatment Regimescited by 13
  29. 2022Multi-disciplinary fairness considerations in machine learning for clinical trialscited by 8
  30. 2023Prediction under interventions: evaluation of counterfactual performance using longitudinal observational datacited by 8
  31. 2020Prediction meets causal inference: the role of treatment in clinical prediction modelscited by 7
  32. 2023A Causal Roadmap for Generating High-Quality Real-World Evidencecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 44; 2026: 26.

73 papers.

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  1. 2025Foundation Models for Causal Inference via Prior-Data Fitted Networkscited by 30
  2. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  3. 2024Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learnercited by 10
  4. 2025Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Datacited by 10
  5. 2025DeCaFlow: A deconfounding causal generative modelcited by 8
  6. 2025Treatment Effect Estimation for Optimal Decision-Makingcited by 7
  7. 2024Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasetscited by 6
  8. 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoptioncited by 6
  9. 2024Continuous Bayesian Model Selection for Multivariate Causal Discoverycited by 5
  10. 2025Efficient and Sharp Off-Policy Learning under Unobserved Confoundingcited by 5
  11. 2025Differentially Private Learners for Heterogeneous Treatment Effectscited by 5
  12. 2025Orthogonal Representation Learning for Estimating Causal Quantitiescited by 4
38 more
  1. 2025Counterfactual Probabilistic Diffusion with Expert Modelscited by 4
  2. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  3. 2025AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Researchcited by 3
  4. 2025A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatmentscited by 3
  5. 2025Overlap-weighted orthogonal meta-learner for treatment effect estimation over timecited by 3
  6. 2026Amortizing Causal Sensitivity Analysis via Prior Data-Fitted Networkscited by 3
  7. 2025PrivATE: Differentially Private Confidence Intervals for Average Treatment Effectscited by 2
  8. 2025LLM-Driven Treatment Effect Estimation Under Inference Time Text Confoundingcited by 2
  9. 2025Causality-informed Anomaly Detection in Partially Observable Sensor Networks: Moving beyond Correlationscited by 2
  10. 2025Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Visioncited by 2
  11. 2025Integrating Probabilistic Trees and Causal Networks for Clinical and Epidemiological Datacited by 1
  12. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  13. 2025Flow-based Generative Modeling of Potential Outcomes and Counterfactualscited by 1
  14. 2025Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameterscited by 1
  15. 2025Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventionscited by 1
  16. 2025Interpretable Clustering with Adaptive Heterogeneous Causal Structure Learning in Mixed Observational Datacited by 1
  17. 2025GDR-learners: Orthogonal Learning of Generative Models for Potential Outcomescited by 1
  18. 2025Overlap-Adaptive Regularization for Conditional Average Treatment Effect Estimationcited by 1
  19. 2025Causal Graph Neural Networks for Healthcarecited by 1
  20. 2025DeepBlip: Estimating Conditional Average Treatment Effects Over Timecited by 1
  21. 2026How Clinicians Think and What AI Can Learn From Itcited by 1
  22. 2026Generalized Bayes for Causal Inferencecited by 1
  23. 2026Observationally Informed Adaptive Causal Experimental Designcited by 1
  24. 2026CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Timecited by 1
  25. 2026Causal multi-modal AI for personalized chemosensitivity predictioncited by 1
  26. 2025Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposiumcited by 0
  27. 2025Unsupervised Structural-Counterfactual Generation under Domain Shiftcited by 0
  28. 2025Sequential Treatment Effect Estimation with Unmeasured Confounderscited by 0
  29. 2025Conditional Average Treatment Effect Estimation Under Hidden Confounderscited by 0
  30. 2025Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Modelscited by 0
  31. 2025Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Recordscited by 0
  32. 2025Causal Machine Learning for Patient-Level Intraoperative Opioid Dose Prediction from Electronic Health Recordscited by 0
  33. 2025Beyond Correlation: Causal Multi-View Unsupervised Feature Selection Learningcited by 0
  34. 2025Towards Privacy-Aware Bayesian Networks: A Credal Approachcited by 0
  35. 2025An Orthogonal Learner for Individualized Outcomes in Markov Decision Processescited by 0
  36. 2025A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR datacited by 0
  37. 2025Estimating Treatment Effects in Networks under Unknown Exposure Mappingscited by 0
  38. 2025Linear Causal Discovery with Interventional Constraintscited by 0

50 most cited of 73 All 73 (JSON)