fairadapt - Fair Data Adaptation with Quantile Preservation
An implementation of the fair data adaptation with quantile preservation described in Plecko & Meinshausen (2019) <arXiv:1911.06685>. The adaptation procedure uses the specified causal graph to pre-process the given training and testing data in such a way to remove the bias caused by the protected attribute. The procedure uses tree ensembles for quantile regression.
Last updated 1 years ago
causal-inferencefairnessmachine-learning
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