  We present an alternative to reweighting techniques for modifying
distributions to account for a desired change in an underlying conditional
distribution, as is often needed to correct for mis-modelling in a simulated
sample. We employ conditional normalizing flows to learn the full conditional
probability distribution from which we sample new events for conditional values
drawn from the target distribution to produce the desired, altered
distribution. In contrast to common reweighting techniques, this procedure is
independent of binning choice and does not rely on an estimate of the density
ratio between two distributions.
  In several toy examples we show that normalizing flows outperform reweighting
approaches to match the distribution of the target.We demonstrate that the
corrected distribution closes well with the ground truth, and a statistical
uncertainty on the training dataset can be ascertained with bootstrapping. In
our examples, this leads to a statistical precision up to three times greater
than using reweighting techniques with identical sample sizes for the source
and target distributions. We also explore an application in the context of high
energy particle physics.
