VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries
This provides a flexible, non-parametric method for causal inference tasks like fairness evaluation, though it is incremental as it builds on existing variational autoencoder and causal graph techniques.
The paper tackles the problem of causal inference without hidden confounders by introducing VACA, a variational graph autoencoder framework that approximates interventional and counterfactual distributions, achieving accurate results on diverse structural causal models and enabling applications in fair classification without performance loss.
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.