mlsanityData to 14 Oct 2026

Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings

arXiv 2610.19211 ↗First posted 13 Oct 2026stat.ML cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain identifiable (i.e., uniquely determined from the available data distribution) under various missingness categories, including Missing Not At Random, and provide a framework to extend correctness guarantees from causal discovery algorithms for complete data to missing data settings. Our results show that identifiability of causal graphs with no unobserved confounders is preserved when (1) missingness mechanisms do not causally interact, (2) no variable directly causes its own missingness, and (3) either faithfulness holds for missingness mechanisms, or they follow generalized linear models. Guided by these theoretical results, we introduce two asymptotically correct algorithms for Additive Noise Models (ANMs) that recover the underlying causal graph and provide consistent parameter estimates.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 13 Oct 2026.

Builds on

12 of 34 references.

References · 12

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
  3. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 140
  4. 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 77
  5. 2018Graphical Models for Processing Missing Datacited by 58
  6. 2018Causal Discovery in the Presence of Missing Datacited by 47
  7. 2017Fast Causal Inference with Non-Random Missingness by Test-Wise Deletioncited by 23
  8. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 22
  9. 2024Optimal Transport for Structure Learning Under Missing Datacited by 6
  10. 2023Identification of Causal Structure in the Presence of Missing Data with Additive Noise Modelcited by 4
  11. 2019Testing Independence under Biased Samplingcited by 2
  12. 2024Generalized Criterion for Identifiability of Additive Noise Models Using Majorizationcited by 1

Built on by

Not cited yet.