Speaker
Description
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 interven-
tions 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+. The code and data can be found at:
https://github.com/wasimahmadpk/deepCausality
