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

• URL: http://arxiv.org/abs/2110.14690v1
• Date: Wed, 27 Oct 2021 18:16:39 GMT
• Title: VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
• Abstract summary: We introduce VacA, a class of variational graph autoencoders for causal inference in the absence of hidden confounders. We apply VacA to evaluate counterfactual fairness in fair classification problems, as well as to learn fair classifiers without compromising performance.
• Score: 11.35838396538348
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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