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. 2022 May 3;12:7166. doi: 10.1038/s41598-022-11012-2

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Model predictions for all patients (dots) in the synthetic medical dataset. S represents a sensitive attribute like Skin color. When the treatment T is biased on S, we observe how much the model relies on S for prediction (left). When source of bias is appropriately included in the model, we can both confirm the source of bias and remove the dependency on sensitive attributes from the predictive models (right).