  Counterfactuals have become a popular technique nowadays for interacting with
black-box machine learning models and understanding how to change a particular
instance to obtain a desired outcome from the model. However, most existing
approaches assume instant materialization of these changes, ignoring that they
may require effort and a specific order of application. Recently, methods have
been proposed that also consider the order in which actions are applied,
leading to the so-called sequential counterfactual generation problem.
  In this work, we propose a model-agnostic method for sequential
counterfactual generation. We formulate the task as a multi-objective
optimization problem and present an evolutionary approach to find optimal
sequences of actions leading to the counterfactuals. Our cost model considers
not only the direct effect of an action, but also its consequences.
Experimental results show that compared to state of the art, our approach
generates less costly solutions, is more efficient, and provides the user with
a diverse set of solutions to choose from.
