  Root Cause Analysis (RCA) aims at identifying the underlying causes of system
faults by uncovering and analyzing the causal structure from complex systems.
It has been widely used in many application domains. Reliable diagnostic
conclusions are of great importance in mitigating system failures and financial
losses. However, previous studies implicitly assume a full observation of the
system, which neglect the effect of partial observation (i.e., missing nodes
and latent malfunction). As a result, they fail in deriving reliable RCA
results. In this paper, we unveil the issues of unobserved confounders and
heterogeneity in partial observation and come up with a new problem of root
cause analysis with partially observed data. To achieve this, we propose PORCA,
a novel RCA framework which can explore reliable root causes under both
unobserved confounders and unobserved heterogeneity. PORCA leverages magnified
score-based causal discovery to efficiently optimize acyclic directed mixed
graph under unobserved confounders. In addition, we also develop a
heterogeneity-aware scheduling strategy to provide adaptive sample weights.
Extensive experimental results on one synthetic and two real-world datasets
demonstrate the effectiveness and superiority of the proposed framework.
