Quasi-regression for visualization and interpretation of black box functions

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “Jiang and Owen (2002) use quasi-regression estimation of black box functions.”
PREDICTIVE LEARNING VIA RULE ENSEMBLES (2008, methodology): “Roosen (1995), Owen (2001) Jiang and Owen (2001) study interaction eﬀects in “black-box” models using the functional ANOVA decomposition of F ( x ) and product measure.”
Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “…Horstemeyer 2006; Regis and Shoemaker 2007a, b), response surface models (Hill and Hunter 1966; Kaufman et al. 1996), support vector machine (Collobert and Bengio 2001), etc. Owen and his group (An and Owen 2001; Jiang and Owen 2002, 2003) developed quasi-regression methods for model approximation.”
CAUSAL INTERPRETATIONS OF BLACK-BOX MODELS (2019, unknown): “Jiang and Owen (2002) noticed that there are at least three notions of variable importance: 1.”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “The Classification and Regression Trees (CART) (Breiman, 2017) algorithm is a divide and conquer algorithm that builds a decision tree from the features of the training data.”