  Spatiotemporal time series is the foundation of understanding human
activities and their impacts, which is usually collected via monitoring sensors
placed at different locations. The collected data usually contains missing
values due to various failures, which have significant impact on data analysis.
To impute the missing values, a lot of methods have been introduced. When
recovering a specific data point, most existing methods tend to take into
consideration all the information relevant to that point regardless of whether
they have a cause-and-effect relationship. During data collection, it is
inevitable that some unknown confounders are included, e.g., background noise
in time series and non-causal shortcut edges in the constructed sensor network.
These confounders could open backdoor paths between the input and output, in
other words, they establish non-causal correlations between the input and
output. Over-exploiting these non-causal correlations could result in
overfitting and make the model vulnerable to noises. In this paper, we first
revisit spatiotemporal time series imputation from a causal perspective, which
shows the causal relationships among the input, output, embeddings and
confounders. Next, we show how to block the confounders via the frontdoor
adjustment. Based on the results of the frontdoor adjustment, we introduce a
novel Causality-Aware SPatiotEmpoRal graph neural network (CASPER), which
contains a novel Spatiotemporal Causal Attention (SCA) and a Prompt Based
Decoder (PBD). PBD could reduce the impact of confounders and SCA could
discover the sparse causal relationships among embeddings. Theoretical analysis
reveals that SCA discovers causal relationships based on the values of
gradients. We evaluate Casper on three real-world datasets, and the
experimental results show that Casper outperforms the baselines and effectively
discovers causal relationships.
