  False Data Injection Attack (FDIA) has become a growing concern in modern
cyber-physical power systems. Most existing FDIA detection techniques project
the raw measurement data into a high-dimensional latent space to separate
normal and attacked samples. These approaches focus more on the statistical
correlations of data values and are therefore susceptible to data distribution
drifts induced by changes in system operating points or changes in FDIA types
and strengths, especially for FDIA localization tasks. Causal inference, on the
other hand, extracts the causality behind the coordinated fluctuations of
different measurements. The causality patterns are determined by fundamental
physical laws such as Ohm's Law and Kirchhoff's Law. They are sensitive to the
violation of physical laws caused by FDIA, but tend to remain stable with the
drift of system operating points. Leveraging this advantage, this paper
proposes a joint FDIA detection and localization framework based on causal
inference and the Graph Attention Network (GAT) to identify the attacked system
nodes. The proposed framework consists of two levels. The lower level uses the
X-learner algorithm to estimate the causality strength between measurements and
generate Measurement Causality Graphs (MCGs). The upper level then applies a
GAT to identify the anomaly patterns in the MCGs. Since the extracted causality
patterns are intrinsically related to the measurements, it is easier for the
upper level to figure out the attacked nodes than the existing FDIA
localization approaches. The performance of the proposed framework is evaluated
on the IEEE 39-bus system. Experimental results show that the causality-based
FDIA detection and localization mechanism is highly interpretable and robust.
