A general framework for inference on algorithm-agnostic variable
importance

A general framework for inference on algorithm-agnostic variable importance

7 April 2020

Papers citing "A general framework for inference on algorithm-agnostic variable importance"

11 / 11 papers shown

Title
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring Hué Sullivan Hurlin Christophe Pérignon Christophe Saurin Sébastien 51 0 0 12 Dec 2022
Efficient nonparametric statistical inference on population feature importance using Shapley values B. Williamson Jean Feng FAtt 41 72 0 16 Jun 2020
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values K. Aas Martin Jullum Anders Løland FAtt TDI 58 622 0 25 Mar 2019
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 124 3,957 0 06 Feb 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,873 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,989 0 04 Mar 2017
Distribution-Free Predictive Inference For Regression Jing Lei M. G'Sell Alessandro Rinaldo Robert Tibshirani Larry A. Wasserman 402 840 0 14 Apr 2016
Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy Alexander Luedtke M. J. van der Laan 254 222 0 24 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Variable importance in binary regression trees and forests H. Ishwaran 193 386 0 15 Nov 2007