  Granger causal modeling is an emerging topic that can uncover Granger causal
relationship behind multivariate time series data. In many real-world systems,
it is common to encounter a large amount of multivariate time series data
collected from different individuals with sharing commonalities. However, there
are ongoing concerns regarding Granger causality's applicability in such large
scale complex scenarios, presenting both challenges and opportunities for
Granger causal structure reconstruction. Existing methods usually train a
distinct model for each individual, suffering from inefficiency and
over-fitting issues. To bridge this gap, we propose an Inductive GRanger cAusal
modeling (InGRA) framework for inductive Granger causality learning and common
causal structure detection on multivariate time series, which exploits the
shared commonalities underlying the different individuals. In particular, we
train one global model for individuals with different Granger causal structures
through a novel attention mechanism, called prototypical Granger causal
attention. The model can detect common causal structures for different
individuals and infer Granger causal structures for newly arrived individuals.
Extensive experiments, as well as an online A/B test on an E-commercial
advertising platform, demonstrate the superior performances of InGRA.
