A Novel Autoencoder Based Approach for Counterfactual Estimation Using Sparsity Constraints

Abstract

Building upon the abduction-action-step scheme and the structural causal model framework, this paper introduces the Conditional Sparse Autoencoder (CSAE), a novel approach for time series counterfactual estimation using encoder-decoder based architectures with a sparsity constraint to disentangle the roles of the inputs in the expected outputs. We benchmark CSAE with Conditional Variational Autoencoder (CVAE), the most widely adopted encoder-decoder architecture for counterfactual estimation, showing that CSAE clearly outperforms CVAE in this domain. Furthermore, we demonstrate the versatility of CSAE by extending it to image-based counterfactual scenarios, obtaining promising results. This work has important implications for a wide range of applications across various domains including finance, healthcare, and transportation, where being able to perform accurate counterfactual estimations is critical for decision-making.