  Counterfactual Explanations (CEs) help address the question: How can the
factors that influence the prediction of a predictive model be changed to
achieve a more favorable outcome from a user's perspective? Thus, they bear the
potential to guide the user's interaction with AI systems since they represent
easy-to-understand explanations. To be applicable, CEs need to be realistic and
actionable. In the literature, various methods have been proposed to generate
CEs. However, the majority of research on CEs focuses on classification
problems where questions like "What should I do to get my rejected loan
approved?" are raised. In practice, answering questions like "What should I do
to increase my salary?" are of a more regressive nature. In this paper, we
introduce a novel method to generate CEs for a pre-trained regressor by first
disentangling the label-relevant from the label-irrelevant dimensions in the
latent space. CEs are then generated by combining the label-irrelevant
dimensions and the predefined output. The intuition behind this approach is
that the ideal counterfactual search should focus on the label-irrelevant
characteristics of the input and suggest changes toward target-relevant
characteristics. Searching in the latent space could help achieve this goal. We
show that our method maintains the characteristics of the query sample during
the counterfactual search. In various experiments, we demonstrate that the
proposed method is competitive based on different quality measures on image and
tabular datasets in regression problem settings. It efficiently returns results
closer to the original data manifold compared to three state-of-the-art
methods, which is essential for realistic high-dimensional machine learning
applications. Our code will be made available as an open-source package upon
the publication of this work.
