一句话：提出时滞感知时空因果推断框架 LagRCA，对齐微服务多时滞故障传播，显著提升根因定位精度。
微服务故障常跨服务传播，且上游故障往往不会立即触发下游告警，症状可能延迟数秒至数分钟出现。现有 RCA 方法多同步分析服务交互，忽略多时滞传播，导致原因与症状错位，使真正上游根因被下游受害者淹没。本文提出 LagRCA，一种时滞感知的时空因果推断框架：它以异构时滞建模故障传播，将上游原因与滞后的下游症状对齐；同时区分服务间因果关系与指标共波动强度，避免共享状态变化被误判为直接因果依赖，并输出可解释传播路径。在公开微服务基准和阿里巴巴大规模真实事故数据上，LagRCA 取得 88.3% 的 Top-5 定位精度，较最优基线高 21.8 个百分点；已部署于阿里巴巴生产微服务环境，提升事故诊断效率并减少人工排查。
Timely root cause analysis (RCA) is essential for stable microservice operations, especially when failures propagate across services. In practice, upstream failures rarely trigger downstream alerts immediately; symptoms at dependent services often emerge seconds or minutes later. However, most existing RCA methods still analyze service interactions synchronously and fail to explicitly account for such multi-lag propagation, which misaligns causes and symptoms and can dilute true upstream culprits while over-ranking downstream victims. In this paper, we present LagRCA, a lag-aware spatio-temporal causal inference framework for microservice RCA. It models failure propagation with heterogeneous time lags, aligning upstream causes with lagged downstream symptoms. At the same time, it disentangles whether one service causally affects another from how strongly their service-level metrics co-fluctuate, preventing shared state changes from being misread as direct causal dependencies. It also produces interpretable propagation paths that help operators understand failure dynamics and act on diagnoses. We evaluate LagRCA on public microservice benchmarks and large-scale real incident data from Alibaba. Experimental results show that LagRCA consistently outperforms state-of-the-art RCA methods, achieving 88.3% top-five localization accuracy (outperforming the best baseline by 21.8 percentage points). Moreover, LagRCA has been deployed in Alibaba's production microservice environment, where it improves incident diagnosis efficiency and reduces manual troubleshooting effort.
@inproceedings{ZhangKZXFWGSLZP26,
title = {Bridging the Delay: Lag-Aware Spatio-Temporal Causal Inference for Microservice Root Cause Analysis},
author = {Shenglin Zhang and Junhua Kuang and Yimeng Zhang and Sibo Xia and Jintao Feng and Jingyu Wang and Wenwei Gu and Yongqian Sun and Wei Li and Liping Zhang and Dan Pei},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805219}
}