mlsanityData to 14 Oct 2026

A Survey of Deep Causal Models and Their Industrial Applications

arXiv 2209.08860 ↗First posted 19 Sep 2022stat.ML cs.LGcited by 10

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Abstract (arXiv)

The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: 1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; 2) we outline some typical applications of causal effect estimation to industry; 3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 86 of its 232 references that are in the map. First posted on arXiv 19 Sep 2022.

Builds on

86 of 232 references.

References · 86

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  2. 2016Learning Representations for Counterfactual Inferencecited by 329
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38 more
  1. 2020Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representationscited by 155
  2. 2021Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithmscited by 144
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50 most cited of 86 All 86 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 7; 2026: 2.

10 papers.

  1. 2023Causal reasoning in typical computer vision taskscited by 12
  2. 2025Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasetscited by 3
  3. 2025Rethinking industrial artificial intelligence: a unified foundation frameworkcited by 1
  4. 2025A Distributionally Robust Framework for Nuisance in Causal Effect Estimationcited by 1
  5. 2025Cross-modal Causal Intervention for Alzheimer's Disease Predictioncited by 1
  6. 2025TSCAN: Context-Aware Uplift Modeling via Two-Stage Training for Online Merchant Business Diagnosiscited by 0
  7. 2025Localising Dropout Variance in Twin Networkscited by 0
  8. 2025DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learningcited by 0
  9. 2026Identifying Causal Effects Using a Single Proxy Variablecited by 0
  10. 2026TRACE: Temporal Reasoning over Context and Evidence for Activity Recognition in Smart Homescited by 0