  Cause-effect analysis is crucial to understand the underlying mechanism of a
system. We propose to exploit model invariance through interventions on the
predictors to infer causality in nonlinear multivariate systems of time series.
We model nonlinear interactions in time series using DeepAR and then expose the
model to different environments using Knockoffs-based interventions to test
model invariance. Knockoff samples are pairwise exchangeable, in-distribution
and statistically null variables generated without knowing the response. We
test model invariance where we show that the distribution of the response
residual does not change significantly upon interventions on non-causal
predictors. We evaluate our method on real and synthetically generated time
series. Overall our method outperforms other widely used causality methods,
i.e, VAR Granger causality, VARLiNGAM and PCMCI+.
