mlsanityData to 14 Oct 2026

Framing Algorithmic Recourse for Anomaly Detection

arXiv 2206.14384 ↗First posted 29 Jun 2022cs.LG cs.AI stat.MEcited by 5

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Abstract (arXiv)

The problem of algorithmic recourse has been explored for supervised machine learning models, to provide more interpretable, transparent and robust outcomes from decision support systems. An unexplored area is that of algorithmic recourse for anomaly detection, specifically for tabular data with only discrete feature values. Here the problem is to present a set of counterfactuals that are deemed normal by the underlying anomaly detection model so that applications can utilize this information for explanation purposes or to recommend countermeasures. We present an approach -- Context preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT), that is effective, scalable, and agnostic to the underlying anomaly detection model. CARAT uses a transformer based encoder-decoder model to explain an anomaly by finding features with low likelihood. Subsequently semantically coherent counterfactuals are generated by modifying the highlighted features, using the overall context of features in the anomalous instance(s). Extensive experiments help demonstrate the efficacy of CARAT.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 30 of its 50 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

30 of 50 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

5 papers.

  1. 2022On Root Cause Localization and Anomaly Mitigation through Causal Inferencecited by 6
  2. 2024Reimagining Anomalies: What If Anomalies Were Normal?cited by 2
  3. 2025DCFO: Density-Based Counterfactuals for Outliers -- Additional Materialcited by 1
  4. 2023Algorithmic Recourse in Abnormal Multivariate Time Seriescited by 0
  5. 2026Algorithmic Recourse of In-Context Learning for Tabular Datacited by 0