  Model averaging techniques based on resampling methods (such as bootstrapping
or subsampling) have been utilized across many areas of statistics, often with
the explicit goal of promoting stability in the resulting output. We provide a
general, finite-sample theoretical result guaranteeing the stability of bagging
when applied to algorithms that return outputs in a general space, so that the
output is not necessarily a real-valued -- for example, an algorithm that
estimates a vector of weights or a density function. We empirically assess the
stability of bagging on synthetic and real-world data for a range of problem
settings, including causal inference, nonparametric regression, and Bayesian
model selection.
