30 citations · 35 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…