30 citations · 40 across the 5 of their papers we have counts for
6 papers
Model free variable importance for high dimensional data
Naofumi Hama, Masayoshi Mase, Art B. Owen
A model-agnostic variable importance method can be used with arbitrary prediction functions. Here we present some model-free methods that do not require access to the prediction fu…
Variable importance without impossible data
Masayoshi Mase, Art B. Owen, Benjamin B. Seiler
The most popular methods for measuring importance of the variables in a black box prediction algorithm make use of synthetic inputs that combine predictor variables from multiple s…
Deletion and Insertion Tests in Regression Models
Naofumi Hama, Masayoshi Mase, Art B. Owen
A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function . The insertion and deletion tests of Petsiuk et…
Cohort Shapley value for algorithmic fairness
Masayoshi Mase, Art B. Owen, Benjamin B. Seiler
Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations. We use…
What makes you unique?
Benjamin B. Seiler, Masayoshi Mase, Art B. Owen
This paper proposes a uniqueness Shapley measure to compare the extent to which different variables are able to identify a subject. Revealing the value of a variable on subject …
Explaining black box decisions by Shapley cohort refinement
Masayoshi Mase, Art B. Owen, Benjamin Seiler
We introduce a variable importance measure to quantify the impact of individual input variables to a black box function. Our measure is based on the Shapley value from cooperative…