Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
This work addresses a long-standing problem in domains like climate science, finance, and healthcare by providing a more comprehensive causal discovery method, though it appears incremental as it builds on existing techniques.
The paper tackled the challenge of discovering causal relationships in time series data by proposing Rhino, a framework that models non-linear relationships with instantaneous effects and history-dependent noise, achieving better discovery performance in synthetic and real-world benchmarks.
Discovering causal relationships between different variables from time series data has been a long-standing challenge for many domains such as climate science, finance, and healthcare. Given the complexity of real-world relationships and the nature of observations in discrete time, causal discovery methods need to consider non-linear relations between variables, instantaneous effects and history-dependent noise (the change of noise distribution due to past actions). However, previous works do not offer a solution addressing all these problems together. In this paper, we propose a novel causal relationship learning framework for time-series data, called Rhino, which combines vector auto-regression, deep learning and variational inference to model non-linear relationships with instantaneous effects while allowing the noise distribution to be modulated by historical observations. Theoretically, we prove the structural identifiability of Rhino. Our empirical results from extensive synthetic experiments and two real-world benchmarks demonstrate better discovery performance compared to relevant baselines, with ablation studies revealing its robustness under model misspecification.