mlsanityData to 7 Oct 2026

Causal Additive Models with Unobserved Causal Paths and Backdoor Paths

arXiv 2502.07646 ↗First posted 11 Feb 2025cs.LG stat.ME stat.MLcited by 5

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Abstract (arXiv)

Causal additive models provide a tractable yet expressive framework for causal discovery in the presence of hidden variables. When unobserved backdoor or causal paths exist between two variables, their causal relationship is often unidentifiable under existing theories. We establish sufficient conditions under which causal directions can be identified in many such cases. These conditions rely on new characterizations of regression sets to determine independence among regression residuals and conditional independencies among observed variables. Building on these results, we introduce a search algorithm that incorporates these innovations and prove its soundness and completeness. Empirical evaluations demonstrate its competitive performance against state-of-the-art methods.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 55 references that are in the map. First posted on arXiv 11 Feb 2025.

Builds on

16 of 55 references.

References · 16

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2019Causality for Machine Learningcited by 242
  4. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  5. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  6. 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
  7. 2019Causal structure based root cause analysis of outlierscited by 65
  8. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
  9. 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
  10. 2013ParceLiNGAM: A causal ordering method robust against latent confounderscited by 29
  11. 2020Causal Discovery with Unobserved Confounding and non-Gaussian Datacited by 24
  12. 2023Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learningcited by 15
4 more
  1. 2023Identification of Causal Structure with Latent Variables Based on Higher Order Cumulantscited by 8
  2. 2024Causal Effect Identification in LiNGAM Models with Latent Confounderscited by 7
  3. 2023Assessing the overall and partial causal well-specification of nonlinear additive noise modelscited by 3
  4. 2024Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosiscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discoverycited by 1
  2. 2026I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variablescited by 1
  3. 2026Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variablescited by 1
  4. 2026Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraintscited by 0
  5. 2026Causal Discovery via Transformed Low-Rank Quantile Surfacescited by 0