mlsanityData to 13 Oct 2026

Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model

arXiv 2205.13085 ↗First posted 25 May 2022stat.ML cs.LG stat.APcited by 38

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Abstract (arXiv)

Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category. A few underlying root causes may nevertheless initiate the development of disease within each patient. We therefore focus on identifying patient-specific root causes of disease, which we equate to the sample-specific predictivity of the exogenous error terms in a structural equation model. We generalize from the linear setting to the heteroscedastic noise model where Y = m(X) + varepsilonσ(X) with non-linear functions m(X) and σ(X) representing the conditional mean and mean absolute deviation, respectively. This model preserves identifiability but introduces non-trivial challenges that require a customized algorithm called Generalized Root Causal Inference (GRCI) to extract the error terms correctly. GRCI recovers patient-specific root causes more accurately than existing alternatives.

Key facts. Cited by 38 papers in the map (data through 13 Oct 2026). Builds on 10 of its 50 references that are in the map. First posted on arXiv 25 May 2022.

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10 of 50 references.

References · 10

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,143
  2. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 282
  3. 2013Causal Discovery with Continuous Additive Noise Modelscited by 253
  4. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
  5. 2019Causal structure based root cause analysis of outlierscited by 65
  6. 2021Why did the distribution change?cited by 37
  7. 2014Distinguishing cause from effect using observational data: methods and benchmarkscited by 33
  8. 2018Causal Inference via Kernel Deviance Measurescited by 22
  9. 2022Identifying Patient-Specific Root Causes of Diseasecited by 8
  10. 2014Justifying Information-Geometric Causal Inferencecited by 6

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 2; 2023: 12; 2024: 5; 2025: 11; 2026: 8.

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  1. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 105
  2. 2023Identifiability of latent-variable and structural-equation models: from linear to nonlinearcited by 58
  3. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  4. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  5. 2023Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queriescited by 23
  6. 2024Root Cause Analysis of Outliers with Missing Structural Knowledgecited by 19
  7. 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environmentcited by 12
  8. 2023Why Do Probabilistic Clinical Models Fail To Transport Between Sites?cited by 9
  9. 2023Counterfactual Formulation of Patient-Specific Root Causes of Diseasecited by 8
  10. 2023Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testingcited by 7
  11. 2024A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discoverycited by 7
  12. 2022Sample-Specific Root Causal Inference with Latent Variablescited by 6
26 more
  1. 2023Heteroscedastic Causal Structure Learningcited by 6
  2. 2023Robust Estimation of Causal Heteroscedastic Noise Modelscited by 6
  3. 2025Distinguishing Cause from Effect with Causal Velocity Modelscited by 6
  4. 2023Structural restrictions in local causal discovery: identifying direct causes of a target variablecited by 5
  5. 2023Order-based Structure Learning with Normalizing Flowscited by 4
  6. 2025Multi-Domain Causal Discovery in Bijective Causal Modelscited by 4
  7. 2025Causal Discovery via Quantile Partial Effectcited by 4
  8. 2023Assessing the overall and partial causal well-specification of nonlinear additive noise modelscited by 3
  9. 2024Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independencecited by 3
  10. 2025ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarioscited by 3
  11. 2025DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Seriescited by 3
  12. 2024Counterfactual-based Root Cause Analysis for Dynamical Systemscited by 2
  13. 2025Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noisescited by 2
  14. 2026DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanismscited by 2
  15. 2025Flow-based Generative Modeling of Potential Outcomes and Counterfactualscited by 1
  16. 2025Skewness-Robust Causal Discovery in Location-Scale Noise Modelscited by 1
  17. 2026Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approachcited by 1
  18. 2026Identifiable Markov Switching Models with Instantaneous Effects and Exponential Familiescited by 1
  19. 2025Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health recordcited by 0
  20. 2025Partially Functional Dynamic Backdoor Diffusion-based Causal Modelcited by 0
  21. 2025On the Identifiability of Causal Graphs with the Invariance Principlecited by 0
  22. 2026Root Cause Analysis of Measurement and Mechanistic Anomaliescited by 0
  23. 2026Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Datacited by 0
  24. 2026Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the 1+K Lower Boundcited by 0
  25. 2026On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Modelscited by 0
  26. 2026Beyond Conditional Independence: Root Cause Analysis with Deep Causal Modelscited by 0