The data science SIG discussed the paper from Larry Wasserman (CMU) on Decorrelated Variable Importance. The standard Leave-Out COvariates (LOCO) estimator is biased. This publication introduces and evaluates four alternatives including methods for optaining confidence intervals. The recommended alternatives are based on non-parametric estimation for decorrelating the set set of interested variables. It provides one-step estimators and the influence functions, a key concept for this estimation. We looked into the related paper Demystifying statistical learning based on efficient influence functions to gain deeper insights into this – frequently overlooked – concept. Additional information can be found in Larry Wasserman’s book All of non-parametric statistics (page 18).
In the next meeting on 6th March we’ll take a look into the topic of Agentic AI.
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