Causal Discovery using Model Invariance through Knockoff Interventions

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 a non-linear multivariate system of time series. We model non-linear interaction in time series using DeepAR and then expose the model to different environments using knockoff intervention to test model invariance. Knockoffs are in-distribution 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 features. We use synthetically generated time series to evaluate and compare our approach with other causality methods. Overall our proposed method outperforms other widely used methods.