@inproceedings{Trifunov:CIW19,
    type = {inproceedings},
    key = {Trifunov:CIW19},
    title = {Causal Link Estimation under Hidden Confounding in Ecological Time Series},
    author = {Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler},
    booktitle = {International Workshop on Climate Informatics (CI)},
    year = {2019},
    abstract = {Understanding the causes of natural phe- nomena is a subject of continuous interest in many research fields such as climate and environmental science. We address the problem of recovering nonlinear causal relationships between time series of ecological variables in the presence of a hidden confounder. We suggest a deep learning approach with domain knowledge integration based on the Causal Effect Variational Autoencoder (CEVAE) which we extend and apply to ecological time series. We compare our method’s performance to that of vector autoregressive Granger Causality (VAR-GC) to emphasize its benefits.},
}