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@ARTICLE{Lagemann:268866,
      author       = {Lagemann, Kai and Lagemann, Christian and Taschler, Bernd
                      and Mukherjee, Sach},
      title        = {{D}eep learning of causal structures in high dimensions
                      under data limitations},
      journal      = {Nature machine intelligence},
      volume       = {5},
      number       = {11},
      issn         = {2522-5839},
      address      = {[London]},
      publisher    = {Springer Nature Publishing},
      reportid     = {DZNE-2024-00365},
      pages        = {1306 - 1316},
      year         = {2023},
      abstract     = {Causal learning is a key challenge in scientific artificial
                      intelligence as it allows researchers to go beyond purely
                      correlative or predictive analyses towards learning
                      underlying cause-and-effect relationships, which are
                      important for scientific understanding as well as for a wide
                      range of downstream tasks. Here, motivated by emerging
                      biomedical questions, we propose a deep neural architecture
                      for learning causal relationships between variables from a
                      combination of high-dimensional data and prior causal
                      knowledge. We combine convolutional and graph neural
                      networks within a causal risk framework to provide an
                      approach that is demonstrably effective under the conditions
                      of high dimensionality, noise and data limitations that are
                      characteristic of many applications, including in
                      large-scale biology. In experiments, we find that the
                      proposed learners can effectively identify novel causal
                      relationships across thousands of variables. Results include
                      extensive (linear and nonlinear) simulations (where the
                      ground truth is known and can be directly compared against),
                      as well as real biological examples where the models are
                      applied to high-dimensional molecular data and their outputs
                      compared against entirely unseen validation experiments.
                      These results support the notion that deep learning
                      approaches can be used to learn causal networks at large
                      scale.},
      cin          = {AG Mukherjee},
      ddc          = {004},
      cid          = {I:(DE-2719)1013030},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1038/s42256-023-00744-z},
      url          = {https://pub.dzne.de/record/268866},
}