@inproceedings{ahmad2024deep,
    type = {inproceedings},
    key = {ahmad2024deep},
    author = {Wasim Ahmad and Maha Shadaydeh and Joachim Denzler},
    title = {Deep Learning-based Group Causal Inference in Multivariate Time-series},
    booktitle = {AAAI Workshop on AI for Time-series (AAAI-WS)},
    year = {2024},
    eprint = {2401.08386},
    archiveprefix = {arXiv},
    primaryclass = {cs.LG},
    abstract = {Causal inference in a nonlinear system of multivariate time series 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.},
    note = {},
    url = {https://arxiv.org/abs/2401.08386},
}