Subgroup identification based on differential effect search—A recursive partitioning method for establishing response to treatment in patient subpopulations

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A Review of Generalizability and Transportability (2021, methodology): “Approaches that also present tests for treatment effect differences between subgroups include Bayesian additive regression trees (BART) and other classification and regression tree (CART) variants (Su et al., 2008, 2009; Lipkovich et al., 2011; Green and Kern, 2012; Athey and Imbens, 2016).”
Using Reinforcement Learning to Personalize Dosing Strategies in a Simulated Cancer Trial with High Dimensional Data (2017, methodology): “3 Variable importance Breiman (2001) originally proposed randomly permuting one variable at a time in the out-of-bag sample for each tree, then calculating the reduction of predictive performance compared to the non-permuted out-of-bag sample.”
Subgroup Identification and Variable Selection from Randomized Clinical Trial Data. (2013, methodology): “Specifically, under the conditions of Theorem 2 in Mammen [1991], we have η̂m(t) = η̂(t) +Op(n −8/15), for all t, where η̂m is our monotone estimator of η and η̂ is the Nadaraya-Watson kernel-weighted average.”