  As machine learning is increasingly used to inform consequential
decision-making (e.g., pre-trial bail and loan approval), it becomes important
to explain how the system arrived at its decision, and also suggest actions to
achieve a favorable decision. Counterfactual explanations -- "how the world
would have (had) to be different for a desirable outcome to occur" -- aim to
satisfy these criteria. Existing works have primarily focused on designing
algorithms to obtain counterfactual explanations for a wide range of settings.
However, one of the main objectives of "explanations as a means to help a
data-subject act rather than merely understand" has been overlooked. In
layman's terms, counterfactual explanations inform an individual where they
need to get to, but not how to get there. In this work, we rely on causal
reasoning to caution against the use of counterfactual explanations as a
recommendable set of actions for recourse. Instead, we propose a shift of
paradigm from recourse via nearest counterfactual explanations to recourse
through minimal interventions, moving the focus from explanations to
recommendations. Finally, we provide the reader with an extensive discussion on
how to realistically achieve recourse beyond structural interventions.
