Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an important challenge is to model both lagged causal relations across observation intervals and faster causal effects that appear as instantaneous relati