  Algorithmic recourse explanations inform stakeholders on how to act to revert
unfavorable predictions. However, in general ML models do not predict well in
interventional distributions. Thus, an action that changes the prediction in
the desired way may not lead to an improvement of the underlying target. Such
recourse is neither meaningful nor robust to model refits. Extending the work
of Karimi et al. (2021), we propose meaningful algorithmic recourse (MAR) that
only recommends actions that improve both prediction and target. We justify
this selection constraint by highlighting the differences between model audit
and meaningful, actionable recourse explanations. Additionally, we introduce a
relaxation of MAR called effective algorithmic recourse (EAR), which, under
certain assumptions, yields meaningful recourse by only allowing interventions
on causes of the target.
