# Case Studies of Causal Discovery from IT Monitoring Time Series

arXiv: https://arxiv.org/abs/2307.15678 · HTML: https://mlsanity.com/paper/2307.15678/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 28 Jul 2023.

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

- [Detecting causal associations in large nonlinear time series datasets](https://mlsanity.com/paper/1702.07007/) (2017)
- [DYNOTEARS: Structure Learning from Time-Series Data](https://mlsanity.com/paper/2002.00498/) (2020)
- [Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes](https://mlsanity.com/paper/1903.01672/) (2019)
- [Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets](https://mlsanity.com/paper/2003.03685/) (2020)
- [High-recall causal discovery for autocorrelated time series with latent confounders](https://mlsanity.com/paper/2007.01884/) (2020)
- [Learning Equivalence Classes of Bayesian Networks Structures](https://mlsanity.com/paper/1302.3566/) (2013)
- [Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition](https://mlsanity.com/paper/2206.05871/) (2022)
- [Causal Discovery in the Presence of Missing Data](https://mlsanity.com/paper/1807.04010/) (2018)
- [Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops](https://mlsanity.com/paper/2303.04038/) (2023)
- [Reconstructing regime-dependent causal relationships from observational time series](https://mlsanity.com/paper/2007.00267/) (2020)
- [Causal Discovery from Conditionally Stationary Time Series](https://mlsanity.com/paper/2110.06257/) (2021)
- [Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms](https://mlsanity.com/paper/2306.08765/) (2023)

## Built on by (most cited first, 14 of 14)

- [Identifiability of total effects from abstractions of time series causal graphs](https://mlsanity.com/paper/2310.14691/) (2023)
- [Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight](https://mlsanity.com/paper/2407.08694/) (2024)
- [Towards identifiability of micro total effects in summary causal graphs with latent confounding: extension of the front-door criterion](https://mlsanity.com/paper/2406.05805/) (2024)
- [Causal Temporal Regime Structure Learning](https://mlsanity.com/paper/2311.01412/) (2023)
- [On the Fly Detection of Root Causes from Observed Data with Application to IT Systems](https://mlsanity.com/paper/2402.06500/) (2024)
- [Average Controlled and Average Natural Micro Direct Effects in Summary Causal Graphs](https://mlsanity.com/paper/2410.23975/) (2024)
- [CAMA: Enhancing Mathematical Reasoning in Large Language Models with Causal Knowledge](https://mlsanity.com/paper/2508.02583/) (2025)
- [Scalable Time-Series Causal Discovery with Approximate Causal Ordering](https://mlsanity.com/paper/2409.05500/) (2024)
- [Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effects](https://mlsanity.com/paper/2505.02781/) (2025)
- [Orientability of Causal Relations in Time Series using Summary Causal Graphs and Faithful Distributions](https://mlsanity.com/paper/2508.21742/) (2025)
- [DMCD: Semantic-Statistical Framework for Causal Discovery](https://mlsanity.com/paper/2602.20333/) (2026)
- [Learning Temporal Causal Structure via Smooth Differentiable Optimization](https://mlsanity.com/paper/2606.03227/) (2026)
- [EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis](https://mlsanity.com/paper/2607.27290/) (2026)
- [Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets](https://mlsanity.com/paper/2609.05150/) (2026)

## Abstract (arXiv)

Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their proper functioning and efficiency, as it allows collecting extensive observational time series data for analysis. The interest in causal discovery is growing in IT monitoring systems as knowing causal relations between different components of the IT system helps in reducing downtime, enhancing system performance and identifying root causes of anomalies and incidents. It also allows proactive prediction of future issues through historical data analysis. Despite its potential benefits, applying causal discovery algorithms on IT monitoring data poses challenges, due to the complexity of the data. For instance, IT monitoring data often contains misaligned time series, sleeping time series, timestamp errors and missing values. This paper presents case studies on applying causal discovery algorithms to different IT monitoring datasets, highlighting benefits and ongoing challenges.