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Causal inference in a nonlinear system of multivariate timeseries isinstrumental in disentangling the intricate web of relationships amongvariables, enabling us to make more accurate predictions and gain deeperinsights into real-world complex systems. Causality methods typically identifythe causal structure of a multivariate system by considering the cause-effectrelationship of each pair of variables while ignoring the collective effect ofa group of variables or interactions involving more than two-time seriesvariables. In this work, we test model invariance by group-level interventionson the trained deep networks to infer causal direction in groups of variables,such as climate and ecosystem, brain networks, etc. Extensive testing withsynthetic and real-world time series data shows a significant improvement ofour method over other applied group causality methods and provides us insightsinto real-world time series. The code for our method can be foundat:https://github.com/wasimahmadpk/gCause.

Deep Learning-based Group Causal Inference in Multivariate Time-series