Model-free knockoffs provide a general and powerful tool to perform high-dimensional controlled variable selection.
The method constructs artificial 'knockoff copies' of the variables in a statistical model and then selects those variables that are clearly better than their corresponding fake copies. A wide range of statistics and machine learning tools can be exploited to estimate the importance of each feature, while guaranteeing finite-sample control of the false discovery rate (FDR). This model-free approach makes it possible to use knockoffs for data originating from any conditional model (Y|X), no matter how high-dimensional, provided that the distribution of the covariates (X) is known.
For more information, see the website below and the accompanying paper.
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