# On Root Cause Localization and Anomaly Mitigation through Causal Inference

arXiv: https://arxiv.org/abs/2212.04031 · HTML: https://mlsanity.com/paper/2212.04031/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Learning Model-Agnostic Counterfactual Explanations for Tabular Data](https://mlsanity.com/paper/1910.09398/) (2019)
- [Algorithmic recourse under imperfect causal knowledge: a probabilistic approach](https://mlsanity.com/paper/2006.06831/) (2020)
- [Explainable Deep One-Class Classification](https://mlsanity.com/paper/2007.01760/) (2020)
- [DoWhy: An End-to-End Library for Causal Inference](https://mlsanity.com/paper/2011.04216/) (2020)
- [Causal structure based root cause analysis of outliers](https://mlsanity.com/paper/1912.02724/) (2019)
- [A Graph Autoencoder Approach to Causal Structure Learning](https://mlsanity.com/paper/1911.07420/) (2019)
- [Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure](https://mlsanity.com/paper/2007.10088/) (2020)
- [Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models](https://mlsanity.com/paper/1805.06230/) (2018)
- [VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queries](https://mlsanity.com/paper/2110.14690/) (2021)
- [A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes](https://mlsanity.com/paper/2206.15033/) (2022)
- [Framing Algorithmic Recourse for Anomaly Detection](https://mlsanity.com/paper/2206.14384/) (2022)

## Built on by (most cited first, 6 of 6)

- [Unifying Explainable Anomaly Detection and Root Cause Analysis in Dynamical Systems](https://mlsanity.com/paper/2502.12086/) (2025)
- [ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios](https://mlsanity.com/paper/2503.01475/) (2025)
- [A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management](https://mlsanity.com/paper/2407.14532/) (2024)
- [Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept](https://mlsanity.com/paper/2407.11056/) (2024)
- [Root Cause Analysis of Measurement and Mechanistic Anomalies](https://mlsanity.com/paper/2601.23026/) (2026)
- [Power Interpretable Causal ODE Networks: A Unified Model for Explainable Anomaly Detection and Root Cause Analysis in Power Systems](https://mlsanity.com/paper/2602.12592/) (2026)

## Abstract (arXiv)

Due to a wide spectrum of applications in the real world, such as security, financial surveillance, and health risk, various deep anomaly detection models have been proposed and achieved state-of-the-art performance. However, besides being effective, in practice, the practitioners would further like to know what causes the abnormal outcome and how to further fix it. In this work, we propose RootCLAM, which aims to achieve Root Cause Localization and Anomaly Mitigation from a causal perspective. Especially, we formulate anomalies caused by external interventions on the normal causal mechanism and aim to locate the abnormal features with external interventions as root causes. After that, we further propose an anomaly mitigation approach that aims to recommend mitigation actions on abnormal features to revert the abnormal outcomes such that the counterfactuals guided by the causal mechanism are normal. Experiments on three datasets show that our approach can locate the root causes and further flip the abnormal labels.