  Transparency is an essential requirement of machine learning based decision
making systems that are deployed in real world. Often, transparency of a given
system is achieved by providing explanations of the behavior and predictions of
the given system. Counterfactual explanations are a prominent instance of
particular intuitive explanations of decision making systems. While a lot of
different methods for computing counterfactual explanations exist, only very
few work (apart from work from the causality domain) considers feature
dependencies as well as plausibility which might limit the set of possible
counterfactual explanations.
  In this work we enhance our previous work on convex modeling for computing
counterfactual explanations by a mechanism for ensuring actionability and
plausibility of the resulting counterfactual explanations.
