mlsanityData to 7 Oct 2026

Causal Discovery from Incomplete Data: A Deep Learning Approach

arXiv 2001.05343 ↗First posted 15 Jan 2020cs.LG stat.MLcited by 26

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Abstract (arXiv)

As systems are getting more autonomous with the development of artificial intelligence, it is important to discover the causal knowledge from observational sensory inputs. By encoding a series of cause-effect relations between events, causal networks can facilitate the prediction of effects from a given action and analyze their underlying data generation mechanism. However, missing data are ubiquitous in practical scenarios. Directly performing existing casual discovery algorithms on partially observed data may lead to the incorrect inference. To alleviate this issue, we proposed a deep learning framework, dubbed Imputated Causal Learning (ICL), to perform iterative missing data imputation and causal structure discovery. Through extensive simulations on both synthetic and real data, we show that ICL can outperform state-of-the-art methods under different missing data mechanisms.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 9; 2024: 7; 2025: 2; 2026: 2.

26 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 34
  3. 2022OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networkscited by 33
  4. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 32
  5. 2023Self-Interpretable Time Series Prediction with Counterfactual Explanationscited by 24
  6. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  7. 2023Taxonomy-Structured Domain Adaptationcited by 6
  8. 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweightingcited by 5
  9. 2024COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Datacited by 5
  10. 2021iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretabilitycited by 4
  11. 2022A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigmscited by 4
  12. 2024psiDAG: Projected Stochastic Approximation Iteration for DAG Structure Learningcited by 3
14 more
  1. 2023Directed Acyclic Graphs With Tearscited by 2
  2. 2023Exploiting Causality Signals in Medical Images: A Pilot Study with Empirical Resultscited by 2
  3. 2023SSL Framework for Causal Inconsistency between Structures and Representationscited by 2
  4. 2024MMLU-SR: A Benchmark for Stress-Testing Reasoning Capability of Large Language Modelscited by 2
  5. 2024Learning Cyclic Causal Models from Incomplete Datacited by 1
  6. 2024Revisiting Differentiable Structure Learning: Inconsistency of ℓ1 Penalty and Beyondcited by 1
  7. 2024MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Datacited by 1
  8. 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 1
  9. 2021Identifiability of AMP chain graph modelscited by 0
  10. 2023Towards Causal Representation Learning and Deconfounding from Indefinite Datacited by 0
  11. 2024Causal Understanding For Video Question Answeringcited by 0
  12. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
  13. 2026Causal Decoding for Hallucination-Resistant Multimodal Large Language Modelscited by 0
  14. 2026Probabilistic Residual Learning for Online Recommendationscited by 0