Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
利用 Knockoff 干预估计隐藏混杂下的时间序列因果联系
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…