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A General Framework for Inference on Algorithm-Agnostic Variable Importance
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DOI：10.1080/01621459.2021.2003200.png)
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In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response-in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection. Supplementary materials for this article are available online.
Keyword:
Machine learning
Statistical inference
Targeted learning
Variable importance
期刊
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IF:
3
论文数:
5.2K
被引数:
4.8W
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