mlsanityData to 14 Oct 2026

VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries

arXiv 2110.14690 ↗First posted 27 Oct 2021stat.ML cs.LGcited by 19

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Abstract (arXiv)

In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and practical framework for approximating interventions (do-operator) and abduction-action-prediction steps. As a result, and as shown by our empirical results, VACA accurately approximates the interventional and counterfactual distributions on diverse SCMs. Finally, we apply VACA to evaluate counterfactual fairness in fair classification problems, as well as to learn fair classifiers without compromising performance.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 37 of its 52 references that are in the map. First posted on arXiv 27 Oct 2021.

Builds on

37 of 52 references.

References · 37

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25 more
  1. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  2. 2019Causality for Machine Learningcited by 243
  3. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 211
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  5. 2018Path-Specific Counterfactual Fairnesscited by 181
  6. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 2; 2025: 5; 2026: 4.

19 papers.

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  1. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 57
  2. 2023Causal normalizing flows: from theory to practicecited by 51
  3. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 43
  4. 2024DiffPO: A causal diffusion model for learning distributions of potential outcomescited by 17
  5. 2022Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Makingcited by 13
  6. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  7. 2023Deep Backtracking Counterfactuals for Causally Compliant Explanationscited by 9
  8. 2025DoubleGen: Debiased Generative Modeling of Counterfactualscited by 9
  9. 2022On Root Cause Localization and Anomaly Mitigation through Causal Inferencecited by 6
  10. 2025CausalMan: A physics-based simulator for large-scale causalitycited by 4
  11. 2025Counterfactual Probabilistic Diffusion with Expert Modelscited by 4
  12. 2023Diffusion Model in Causal Inference with Unmeasured Confounderscited by 3
7 more
  1. 2026CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactualscited by 1
  2. 2026Debiased Counterfactual Generation via Flow Matching from Observationscited by 1
  3. 2024Teleporter Theory: A General and Simple Approach for Modeling Cross-World Counterfactual Causalitycited by 0
  4. 2025Causal Representation Learning from Network Datacited by 0
  5. 2025Neurocircuitry-Inspired Hierarchical Graph Causal Attention Networks for Explainable Depression Identificationcited by 0
  6. 2026JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertaintycited by 0
  7. 2026Amortized Counterfactual Prediction with Graph Neural Networkscited by 0