mlsanityData to 7 Oct 2026

Full Law Identification In Graphical Models Of Missing Data: Completeness Results

arXiv 2004.04872 ↗First posted 10 Apr 2020stat.ME cs.LGcited by 26

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Abstract (arXiv)

Missing data has the potential to affect analyses conducted in all fields of scientific study, including healthcare, economics, and the social sciences. Several approaches to unbiased inference in the presence of non-ignorable missingness rely on the specification of the target distribution and its missingness process as a probability distribution that factorizes with respect to a directed acyclic graph. In this paper, we address the longstanding question of the characterization of models that are identifiable within this class of missing data distributions. We provide the first completeness result in this field of study -- necessary and sufficient graphical conditions under which, the full data distribution can be recovered from the observed data distribution. We then simultaneously address issues that may arise due to the presence of both missing data and unmeasured confounding, by extending these graphical conditions and proofs of completeness, to settings where some variables are not just missing, but completely unobserved.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Apr 2020.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 7; 2024: 4; 2025: 6; 2026: 3.

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  1. 2021MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanismscited by 50
  2. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 21
  3. 2022Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populationscited by 14
  4. 2023Copula-Based Deep Survival Models for Dependent Censoringcited by 12
  5. 2023Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settingscited by 9
  6. 2024Optimal Transport for Structure Learning Under Missing Datacited by 5
  7. 2021Variational Gibbs Inference for Statistical Model Estimation from Incomplete Datacited by 4
  8. 2022On Testability and Goodness of Fit Tests in Missing Data Modelscited by 4
  9. 2023Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanismscited by 4
  10. 2023Evaluation of Active Feature Acquisition Methods for Static Feature Settingscited by 4
  11. 2025Learning-To-Measure: In-Context Active Feature Acquisitioncited by 4
  12. 2021Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesiscited by 3
14 more
  1. 2023Identification of Causal Structure in the Presence of Missing Data with Additive Noise Modelcited by 3
  2. 2023Correcting for Selection Bias and Missing Response in Regression using Privileged Informationcited by 2
  3. 2023Identification and Estimation for Nonignorable Missing Data: A Data Fusion Approachcited by 1
  4. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  5. 2025Learning High-dimensional Gaussians from Censored Datacited by 1
  6. 2025Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Supportcited by 1
  7. 2024Zero Inflation as a Missing Data Problem: a Proxy-based Approachcited by 0
  8. 2024Multi-armed Bandits with Missing Outcomecited by 0
  9. 2025Domain Adaptation Under MNAR Missingnesscited by 0
  10. 2025Markov Missing Graph: A Graphical Approach for Missing Data Imputationcited by 0
  11. 2025Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"cited by 0
  12. 2026Identifiable Deep Latent Variable Models for MNAR Datacited by 0
  13. 2026Missingness-MDPs: Bridging the Theory of Missing Data and POMDPscited by 0
  14. 2026Counterfactual Evaluation of Temporal Observation Protocolscited by 0