# ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)

arXiv: https://arxiv.org/abs/1903.08132 · HTML: https://mlsanity.com/paper/1903.08132/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

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

- [Aegis: Automated Error Generation and Attribution for Multi-Agent Systems](https://mlsanity.com/paper/2509.14295/) (2025)
- [RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems](https://mlsanity.com/paper/2503.04252/) (2025)
- [BALANCE: Bayesian Linear Attribution for Root Cause Localization](https://mlsanity.com/paper/2301.13572/) (2023)
- [ProRCA: A Causal Python Package for Actionable Root Cause Analysis in Real-world Business Scenarios](https://mlsanity.com/paper/2503.01475/) (2025)
- [PORCA: Root Cause Analysis with Partially Observed Data](https://mlsanity.com/paper/2407.05869/) (2024)
- [Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection](https://mlsanity.com/paper/2202.12720/) (2022)

## Abstract (arXiv)

We present ExplainIt!, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. ExplainIt! then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how ExplainIt! had helped us resolve over 30 performance issues in a commercial product since late 2014, of which we discuss a few cases in detail.