  Granger causality, commonly used for inferring causal structures from time
series data, has been adopted in widespread applications across various fields
due to its intuitive explainability and high compatibility with emerging deep
neural network prediction models. To alleviate challenges in better deciphering
causal structures unambiguously from time series, the use of interventional
data has become a practical approach. However, existing methods have yet to be
explored in the context of imperfect interventions with unknown targets, which
are more common and often more beneficial in a wide range of real-world
applications. Additionally, the identifiability issues of Granger causality
with unknown interventional targets in complex network models remain unsolved.
Our work presents a theoretically-grounded method that infers Granger causal
structure and identifies unknown targets by leveraging heterogeneous
interventional time series data. We further illustrate that learning Granger
causal structure and recovering interventional targets can mutually promote
each other. Comparative experiments demonstrate that our method outperforms
several robust baseline methods in learning Granger causal structure from
interventional time series data.
