All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously. Fisher, A., Rudin, C., & Dominici, F. Journal of Machine Learning Research, 20(177):1–81, 2019.

Paper bibtex

@article{fisher_all_2019,
	title = {All {Models} are {Wrong}, but {Many} are {Useful}: {Learning} a {Variable}'s {Importance} by {Studying} an {Entire} {Class} of {Prediction} {Models} {Simultaneously}},
	volume = {20},
	issn = {1533-7928},
	shorttitle = {All {Models} are {Wrong}, but {Many} are {Useful}},
	url = {http://jmlr.org/papers/v20/18-760.html},
	number = {177},
	urldate = {2020-08-20},
	journal = {Journal of Machine Learning Research},
	author = {Fisher, Aaron and Rudin, Cynthia and Dominici, Francesca},
	year = {2019},
	keywords = {Statistics - Methodology},
	pages = {1--81},
}

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