mlsanityData to 14 Oct 2026

MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms

arXiv 2111.03187 ↗First posted 4 Nov 2021cs.LGcited by 51

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Abstract (arXiv)

Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a regularization scheme that encourages any baseline imputation method to be causally consistent with the underlying data generating mechanism. Our proposal is a causally-aware imputation algorithm (MIRACLE). MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism, encouraging imputation to be consistent with the causal structure of the data. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation over a variety of benchmark methods across all three missingness scenarios: at random, completely at random, and not at random.

Key facts. Cited by 51 papers in the map (data through 14 Oct 2026). Builds on 14 of its 47 references that are in the map. First posted on arXiv 4 Nov 2021.

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14 of 47 references.

References · 14

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  3. 2019Learning Sparse Nonparametric DAGscited by 207
  4. 2020Handling Missing Data with Graph Representation Learningcited by 133
  5. 2013Generalized Denoising Auto-Encoders as Generative Modelscited by 113
  6. 2020Missing Data Imputation using Optimal Transportcited by 100
  7. 2018Causal Discovery in the Presence of Missing Datacited by 47
  8. 2020CASTLE: Regularization via Auxiliary Causal Graph Discoverycited by 41
  9. 2020Full Law Identification In Graphical Models Of Missing Data: Completeness Resultscited by 26
  10. 2017MIDA: Multiple Imputation using Denoising Autoencoderscited by 20
  11. 2019Identification In Missing Data Models Represented By Directed Acyclic Graphscited by 19
  12. 2013What Is Meant by "Missing at Random"?cited by 16
2 more
  1. 2018Learning Dynamics of Linear Denoising Autoencoderscited by 16
  2. 2013Provable Bounds for Learning Some Deep Representationscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 11; 2023: 6; 2024: 15; 2025: 10; 2026: 9.

51 papers.

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  1. 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 674
  2. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 83
  3. 2024Deep Learning for Multivariate Time Series Imputation: A Surveycited by 57
  4. 2022MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray imagescited by 54
  5. 2023ReMasker: Imputing Tabular Data with Masked Autoencodingcited by 37
  6. 2023Transformed Distribution Matching for Missing Value Imputationcited by 31
  7. 2024LLM-Forest: Ensemble Learning of LLMs with Graph-Augmented Prompts for Data Imputationcited by 25
  8. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 22
  9. 2023Causal Deep Learningcited by 20
  10. 2022The Missing Indicator Method: From Low to High Dimensionscited by 19
  11. 2024DiffPuter: Empowering Diffusion Models for Missing Data Imputationcited by 19
  12. 2024Self-Supervision Improves Diffusion Models for Tabular Data Imputationcited by 16
38 more
  1. 2022Data Imputation with Iterative Graph Reconstructioncited by 15
  2. 2024Diffusion Models for Tabular Data Imputation and Synthetic Data Generationcited by 15
  3. 2022Handling Missing Data via Max-Entropy Regularized Graph Autoencodercited by 12
  4. 2022To Impute or not to Impute? Missing Data in Treatment Effect Estimationcited by 9
  5. 2022MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequencescited by 8
  6. 2024Optimal Transport for Structure Learning Under Missing Datacited by 6
  7. 2024Knockout: A simple way to handle missing inputscited by 4
  8. 2024In-Database Data Imputationcited by 3
  9. 2024Rethinking the Diffusion Models for Numerical Tabular Data Imputation from the Perspective of Wasserstein Gradient Flowcited by 3
  10. 2025Incomplete Graph Learning: A Comprehensive Surveycited by 3
  11. 2023Adversarial Learning for Feature Shift Detection and Correctioncited by 2
  12. 2025Impute-MACFM: Imputation based on Mask-Aware Flow Matchingcited by 2
  13. 2022Navigating causal deep learningcited by 1
  14. 2023Data Imputation from the Perspective of Graph Dirichlet Energycited by 1
  15. 2024Learning Cyclic Causal Models from Incomplete Datacited by 1
  16. 2024New User Event Prediction Through the Lens of Causal Inferencecited by 1
  17. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  18. 2025Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions in Transformercited by 1
  19. 2025Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Supportcited by 1
  20. 2025TabINR: An Implicit Neural Representation Framework for Tabular Data Imputationcited by 1
  21. 2026Learning Causal Orderings for In-Context Tabular Predictioncited by 1
  22. 2022Differentiable and Transportable Structure Learningcited by 0
  23. 2023Deep Generative Imputation Model for Missing Not At Random Datacited by 0
  24. 2024DPGAN: A Dual-Path Generative Adversarial Network for Missing Data Imputation in Graphscited by 0
  25. 2024M3-Impute: Mask-guided Representation Learning for Missing Value Imputationcited by 0
  26. 2024Enhancing Missing Data Imputation through Combined Bipartite Graph and Complete Directed Graphcited by 0
  27. 2025Learning Over Dirty Data with Minimal Repairscited by 0
  28. 2025Prediction Models That Learn to Avoid Missing Valuescited by 0
  29. 2025IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)cited by 0
  30. 2025Kernel Representation and Similarity Measure for Incomplete Datacited by 0
  31. 2025Masked Autoencoder Joint Learning for Robust Spitzoid Tumor Classificationcited by 0
  32. 2026Data-driven Lake Water Quality Forecasting for Time Series with Missing Data using Machine Learningcited by 0
  33. 2026Analyzing and Improving Diffusion Models for Time-Series Data Imputation: A Proximal Recursion Perspectivecited by 0
  34. 2026SATORIS-N: Spectral Analysis based Traffic Observation Recovery via Informed Subspaces and Nuclear-norm minimizationcited by 0
  35. 2026Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusioncited by 0
  36. 2026Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recoverycited by 0
  37. 2026From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Datacited by 0
  38. 2026Learning Causal Normalizing Flows from Incomplete Data via Observed-Data Likelihoodcited by 0

50 most cited of 51 All 51 (JSON)