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@ARTICLE{Komeyer:1034775,
      author       = {Komeyer, Vera and Eickhoff, Simon and Rathkopf, Charles and
                      Grefkes, Christian and Patil, Kaustubh and Raimondo,
                      Federico},
      title        = {{C}orrect deconfounding enables causal machine learning for
                      precision medicine and beyond},
      reportid     = {FZJ-2024-07529},
      year         = {2024},
      abstract     = {Artificial intelligence holds promise for individualized
                      medicine. Yet, predictive models in the neurobiomedical
                      domain suffer from a lack of generalizability and
                      replicability so that transitioning models from prototyping
                      to clinical applications still poses challenges. Key
                      contributors to these challenges are confounding effects; in
                      particular the oftentimes purely statistical perspective on
                      confounding. However, complementing these statistical
                      considerations with causal reasoning from domain knowledge
                      can make predictive models a tool for causal biomedical
                      inference beyond associative insights. Such causal insights
                      give answers to biomedical questions of how and why,
                      arguably what most biomedical investigations ultimately seek
                      for. Here, we suggest a 5-step approach for targeted,
                      context-informed deconfounding. We exemplify the 5-step
                      approach with a real-world neurobiomedical predictive task
                      using data from the UK Biobank. The core of this approach
                      constitutes a bottom-up causal analysis to identify a
                      correct set of deconfounders and the appropriate
                      deconfounding method for a given causal predictive
                      endeavour. Using the 5-step approach to combine causal with
                      statistical confounder considerations can make predictive
                      models based on observational (big) data a technique
                      comparable to Randomized Control Trials (RCTs). Through
                      causally motivated deconfounding we aim at facilitating the
                      development of reliable and trustworthy AI as a medical
                      tool. In addition, we aim to foster the relevance of low
                      performing or even null result models if they originate from
                      a “skilful interrogation of nature”, i.e. a
                      deconfounding strategy derived from an adequate causal and
                      statistical analysis. Ultimately, causal predictive
                      modelling through appropriate deconfounding can contribute
                      to mutual recursive feedback loops of causal insights across
                      disciplines, scales and species that enable the field to
                      disentangle the cause-effect structure of neurobiomedical
                      mechanisms.},
      cin          = {INM-7 / INM-3},
      cid          = {I:(DE-Juel1)INM-7-20090406 / I:(DE-Juel1)INM-3-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / DFG project G:(GEPRIS)431549029 - SFB 1451:
                      Schlüsselmechanismen normaler und krankheitsbedingt
                      gestörter motorischer Kontrolle (431549029)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)POF4-5251 /
                      G:(GEPRIS)431549029},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.09.20.24314055},
      url          = {https://juser.fz-juelich.de/record/1034775},
}