  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.
