Deep Learning-based Group Causal Inference in Multivariate Time-series
This work addresses the challenge of understanding complex causal relationships in domains like climate, ecosystems, and brain networks, but it appears incremental as it builds on existing group causality methods with a novel deep learning integration.
The paper tackles the problem of causal inference in multivariate time series by addressing the limitation of existing methods that ignore group-level effects, proposing a deep learning-based approach that uses group-level interventions on trained networks to infer causal directions in groups of variables. The result shows significant improvement over other group causality methods in tests with synthetic and real-world data.
Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accurate predictions and gain deeper insights into real-world complex systems. Causality methods typically identify the causal structure of a multivariate system by considering the cause-effect relationship of each pair of variables while ignoring the collective effect of a group of variables or interactions involving more than two-time series variables. In this work, we test model invariance by group-level interventions on the trained deep networks to infer causal direction in groups of variables, such as climate and ecosystem, brain networks, etc. Extensive testing with synthetic and real-world time series data shows a significant improvement of our method over other applied group causality methods and provides us insights into real-world time series. The code for our method can be found at:https://github.com/wasimahmadpk/gCause.