Effects of bagging and bias correction on estimators defined by estimating equations

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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, background): “As observed by Buja and Stuetzle (2006), Chen and Hall (2003), Friedman and Hall (2007) and others, estimators of this form can exhibit surprising properties even in simple situations; meanwhile, using subsampling rather than bootstrap sampling has been found to avoid several pitfalls (e.g.,…”
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife (2013, methodology): “There has been considerable interest in studying classes of models for which bagging can achieve meaningful variance reduction, and also in outlining situations where bagging can fail completely (e.g., Skurichina and Duin, 1998; Bühlmann and Yu, 2002; Chen and Hall, 2003; Buja and Stuetzle, 2006; Fr”
OBSERVATIONS ON BAGGING (2006, methodology): “This point even applies to Buhlmann and Yu’s (2002) treatment of bagged stumps and trees because their notion of bias refers not”
Asymptotic Theory for Random Forests (2014, background): “As observed by Buja and Stuetzle [10], Chen and Hall [11], Friedman and Hall [17] and others, estimators of this form can exhibit surprising properties even in simple situations.”