  The increasing deployment of machine learning as well as legal regulations
such as EU's GDPR cause a need for user-friendly explanations of decisions
proposed by machine learning models. Counterfactual explanations are considered
as one of the most popular techniques to explain a specific decision of a
model. While the computation of "arbitrary" counterfactual explanations is well
studied, it is still an open research problem how to efficiently compute
plausible and feasible counterfactual explanations. We build upon recent work
and propose and study a formal definition of plausible counterfactual
explanations. In particular, we investigate how to use density estimators for
enforcing plausibility and feasibility of counterfactual explanations. For the
purpose of efficient computations, we propose convex density constraints that
ensure that the resulting counterfactual is located in a region of the data
space of high density.
