Bootstrap-Based Variance Estimators for A Bagging Predictor.

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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests (2015, background): “Green and Kern [2012] and Hill [2011] have proposed the use of forest-based algorithms for estimating heterogeneous treatment effects.”
Confidence Intervals for Random Forests: The Jackknife and the Infinitesimal Jackknife (2013, methodology): “Other approaches that rely on forming second-order bootstrap replicates have been studied by Duan (2011) and Sexton and Laake (2009).”
Asymptotic Theory for Random Forests (2014, result): “Our work builds on recent contributions by Duan [13], Sexton and Laake [27] and Wager et al. [30], who sought to move beyond a black-box analysis of random forests by providing variance estimates for their predictions based on ideas like the bootstrap and the jackknife.”