Longitudinal Counterfactuals: Constraints and Opportunities
Alexander Asemota Giles Hooker
Counterfactual explanations are a common approach to providing recourse to
data subjects. However, current methodology can produce counterfactuals that
cannot be achieved by the subject, making the use of counterfactuals for
recourse difficult to justify in practice. Though there is agreement that
plausibility is an important quality when using counterfactuals for algorithmic
recourse, ground truth plausibility continues to be difficult to quantify. In
this paper, we propose using longitudinal data to assess and improve
plausibility in counterfactuals. In particular, we develop a metric that
compares longitudinal differences to counterfactual differences, allowing us to
evaluate how similar a counterfactual is to prior observed changes.
Furthermore, we use this metric to generate plausible counterfactuals. Finally,
we discuss some of the inherent difficulties of using counterfactuals for
recourse.