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Improvement-Focused Causal Recourse (ICR)

MCML Authors

Gunnar König

Dr.

* Former Member

→ Group Bernd Bischl
Statistical Learning and Data Science

Abstract

Algorithmic recourse recommendations, such as Karimi et al.’s (2021) causal recourse (CR), inform stakeholders of how to act to revert unfavorable decisions. However, there are actions that lead to acceptance (i.e., revert the model’s decision) but do not lead to improvement (i.e., may not revert the underlying real-world state). To recommend such actions is to recommend fooling the predictor. We introduce a novel method, Improvement-Focused Causal Recourse (ICR), which involves a conceptual shift: Firstly, we require ICR recommendations to guide toward improvement. Secondly, we do not tailor the recommendations to be accepted by a specific predictor. Instead, we leverage causal knowledge to design decision systems that predict accurately pre- and post-recourse. As a result, improvement guarantees translate into acceptance guarantees. We demonstrate that given correct causal knowledge ICR, in contrast to existing approaches, guides toward both acceptance and improvement.

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