VACA Designing Variational Graph Autoencoders for Causal Queries

@conference{Sanchez22VACA,
  title = {VACA Designing Variational Graph Autoencoders for Causal Queries},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  abstract = {In this paper, we introduce VACA, a novel class of vari-
  ational graph autoencoders for causal inference in the ab-
  sence of hidden confounders, when only observational data
  and the causal graph are available. Without making any para-
  metric 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 ap-
  proximates the interventional and counterfactual distributions
  on diverse SCMs. Finally, we apply VACA to evaluate coun-
  terfactual fairness in fair classification problems, as well as to
  learn fair classifiers without compromising performance.},
  volume = {36},
  number = {7},
  year = {2022},
  author = {Sanchez-Martin, Pablo and Rateike, Miriam and Valera, Isabel},
  url = {https://ojs.aaai.org/index.php/AAAI/article/download/20789/20548}
}