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arXiv AI By Sejong Oh

Actionable CBFI: Prescriptive Interventional Diagnosis and Interaction-Aware Escalation for Sparse Causal Algorithmic Recourse

Read the original on arXiv AI →

The paper introduces Actionable Case-Based Feature Importance (A-CBFI), a method that transforms the descriptive decomposition of Localized-CBFI into a prescriptive, interventional diagnosis using a structural causal model. A-CBFI selects the cheapest single action when possible and only considers joint actions if a single action cannot achieve the desired outcome. Experiments on ten tabular benchmarks show that A-CBFI matches or exceeds the success of untargeted causal beam search while prescribing fewer direct actions and requiring fewer model evaluations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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