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.