  Causal inference in a nonlinear system of multivariate timeseries is
instrumental in disentangling the intricate web of relationships among
variables, enabling us to make more accurate predictions and gain deeper
insights into real-world complex systems. Causality methods typically identify
the causal structure of a multivariate system by considering the cause-effect
relationship of each pair of variables while ignoring the collective effect of
a group of variables or interactions involving more than two-time series
variables. In this work, we test model invariance by group-level interventions
on the trained deep networks to infer causal direction in groups of variables,
such as climate and ecosystem, brain networks, etc. Extensive testing with
synthetic and real-world time series data shows a significant improvement of
our method over other applied group causality methods and provides us insights
into real-world time series. The code for our method can be found
at:https://github.com/wasimahmadpk/gCause.
