VACA: Designing Variational Graph Autoencoders for Causal Queries
DOI:
https://doi.org/10.1609/aaai.v36i7.20789Keywords:
Machine Learning (ML)Abstract
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.Downloads
Published
2022-06-28
How to Cite
Sánchez-Martin, P., Rateike, M., & Valera, I. (2022). VACA: Designing Variational Graph Autoencoders for Causal Queries. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 8159-8168. https://doi.org/10.1609/aaai.v36i7.20789
Issue
Section
AAAI Technical Track on Machine Learning II