Abstract
In this paper, we discuss the importance of considering causal relations in the development of machine learning solutions to prevent factors hampering the robustness and generalisation capacity of the models, such as induced biases. This issue often arises when the algorithm decision is affected by confounding factors. In this work, we argue that the integration of research assumptions as causal relationships can help identify potential confounders. Together with metadata information, it can enable meta-comparison of data acquisition pipelines. We call for standardised meta-information practices as a crucial step for proper machine learning solutions development, validation, and data sharing. Such practices include detailing the data acquisition process, aiming for automatic integration of causal relationships and actionable metadata.
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Acknowledgments
The authors would like to thank Andreas Husch and Matias Bossa for their support and review efforts. Beatriz Garcia Santa Cruz work is supported by the FNR-PRIDE17/12244779/PARK-QC and Pelican award from the Fondation du Pelican de Mie et Pierre Hippert-Faber.
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Garcia Santa Cruz, B., Vega, C., Hertel, F. (2022). The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions. In: Chicco, D., et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2021. Lecture Notes in Computer Science(), vol 13483. Springer, Cham. https://doi.org/10.1007/978-3-031-20837-9_16
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