---
res:
  bibo_abstract:
  - "Causal representation learning promises to extend causal models to hidden causal\r\nvariables
    from raw entangled measurements. However, most progress has focused\r\non proving
    identifiability results in different settings, and we are not aware of any\r\nsuccessful
    real-world application. At the same time, the field of dynamical systems\r\nbenefited
    from deep learning and scaled to countless applications but does not allow\r\nparameter
    identification. In this paper, we draw a clear connection between the two\r\nand
    their key assumptions, allowing us to apply identifiable methods developed\r\nin
    causal representation learning to dynamical systems. At the same time, we can\r\nleverage
    scalable differentiable solvers developed for differential equations to build\r\nmodels
    that are both identifiable and practical. Overall, we learn explicitly controllable
    models that isolate the trajectory-specific parameters for further downstream\r\ntasks
    such as out-of-distribution classification or treatment effect estimation. We\r\nexperiment
    with a wind simulator with partially known factors of variation. We\r\nalso apply
    the resulting model to real-world climate data and successfully answer\r\ndownstream
    causal questions in line with existing literature on climate change.\r\nCode is
    available at https://github.com/CausalLearningAI/crl-dynamical-systems.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Dingling
      foaf_name: Yao, Dingling
      foaf_surname: Yao
      foaf_workInfoHomepage: http://www.librecat.org/personId=d3e02e50-48a8-11ee-8f62-c108061797fa
  - foaf_Person:
      foaf_givenName: Caroline J
      foaf_name: Muller, Caroline J
      foaf_surname: Muller
      foaf_workInfoHomepage: http://www.librecat.org/personId=f978ccb0-3f7f-11eb-b193-b0e2bd13182b
    orcid: 0000-0001-5836-5350
  - foaf_Person:
      foaf_givenName: Francesco
      foaf_name: Locatello, Francesco
      foaf_surname: Locatello
      foaf_workInfoHomepage: http://www.librecat.org/personId=26cfd52f-2483-11ee-8040-88983bcc06d4
    orcid: 0000-0002-4850-0683
  bibo_volume: 37
  dct_date: 2024^xs_gYear
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Marrying causal representation learning with dynamical systems for science@
...