Public articles linked to the same research event.
arXiv Addressing the challenge of jointly modeling instantaneous within-interval causal effects and lagged cross-interval causal effects while accounting for nonstationarity in time-series causal representation learning, this work establishes sufficient conditions for identifying latent states and their instantaneous and lagged causal structures using an observed auxiliary variable such as time or a condition label, and proposes iCReN, a contrastive-learning framework with discrete or continuous auxiliary variables that learns latent representations and estimates both causal structures, with experiments showing accurate recovery of latent states and both structures on synthetic data and utility of the learned representations for downstream forecasting on real-world data.
Addressing the challenge of jointly modeling instantaneous within-interval causal effects and lagged cross-interval causal effects while accounting for nonstationarity in time-series causal representation learning, this work establishes sufficient conditions for identifying latent states and their instantaneous and lagged causal structures using an observed auxiliary variable such as time or a condition label, and proposes iCReN, a contrastive-learning framework with discrete or continuous auxiliary variables that learns latent representations and estimates both causal structures, with experiments showing accurate recovery of latent states and both structures on synthetic data and utility of the learned representations for downstream forecasting on real-world data.
Addressing the challenge of jointly modeling instantaneous within-interval causal effects and lagged cross-interval causal effects while accounting for nonstationarity in time-series causal representation learning, this work establishes sufficient conditions for identifying latent states and their instantaneous and lagged causal structures using an observed auxiliary variable such as time or a condition label, and proposes iCReN, a contrastive-learning framework with discrete or continuous auxiliary variables that learns latent representations and estimates both causal structures, with experiments showing accurate recovery of latent states and both structures on synthetic data and utility of the learned representations for downstream forecasting on real-world data.
Addressing the challenge of jointly modeling instantaneous within-interval causal effects and lagged cross-interval causal effects while accounting for nonstationarity in time-series causal representation learning, this work establishes sufficient conditions for identifying latent states and their instantaneous and lagged causal structures using an observed auxiliary variable such as time or a condition label, and proposes iCReN, a contrastive-learning framework with discrete or continuous auxiliary variables that learns latent representations and estimates both causal structures, with experiments showing accurate recovery of latent states and both structures on synthetic data and utility of the learned representations for downstream forecasting on real-world data.