  Latent variables often mask cause-effect relationships in observational data
which provokes spurious links that may be misinterpreted as causal. This
problem sparks great interest in the fields such as climate science and
economics. We propose to estimate confounded causal links of time series using
Sequential Causal Effect Variational Autoencoder (SCEVAE) while applying
Knockoff interventions. Knockoff variables have the same distribution as the
originals and preserve the correlation to other variables. This allows for
counterfactuals that are more faithful to the observational distribution. We
show the advantage of Knockoff interventions by applying SCEVAE to synthetic
datasets with both linear and nonlinear causal links. Moreover, we apply SCEVAE
with Knockoffs to real aerosol-cloud-climate observational time series data. We
compare our results on synthetic data to those of a time series deconfounding
method both with and without estimated confounders. We show that our method
outperforms this benchmark by comparing both methods to the ground truth. For
the real data analysis, we rely on expert knowledge of causal links and
demonstrate how using suitable proxy variables improves the causal link
estimation in the presence of hidden confounders.
