  Explainable Artificial Intelligence (XAI) is a set of techniques that allows
the understanding of both technical and non-technical aspects of Artificial
Intelligence (AI) systems. XAI is crucial to help satisfying the increasingly
important demand of \emph{trustworthy} Artificial Intelligence, characterized
by fundamental characteristics such as respect of human autonomy, prevention of
harm, transparency, accountability, etc. Within XAI techniques, counterfactual
explanations aim to provide to end users a set of features (and their
corresponding values) that need to be changed in order to achieve a desired
outcome. Current approaches rarely take into account the feasibility of actions
needed to achieve the proposed explanations, and in particular they fall short
of considering the causal impact of such actions. In this paper, we present
Counterfactual Explanations as Interventions in Latent Space (CEILS), a
methodology to generate counterfactual explanations capturing by design the
underlying causal relations from the data, and at the same time to provide
feasible recommendations to reach the proposed profile. Moreover, our
methodology has the advantage that it can be set on top of existing
counterfactuals generator algorithms, thus minimising the complexity of
imposing additional causal constrains. We demonstrate the effectiveness of our
approach with a set of different experiments using synthetic and real datasets
(including a proprietary dataset of the financial domain).
