  There is a growing use of neural network classifiers as unbinned,
high-dimensional (and variable-dimensional) reweighting functions. To date, the
focus has been on marginal reweighting, where a subset of features are used for
reweighting while all other features are integrated over. There are some
situations, though, where it is preferable to condition on auxiliary features
instead of marginalizing over them. In this paper, we introduce neural
conditional reweighting, which extends neural marginal reweighting to the
conditional case. This approach is particularly relevant in high-energy physics
experiments for reweighting detector effects conditioned on particle-level
truth information. We leverage a custom loss function that not only allows us
to achieve neural conditional reweighting through a single training procedure,
but also yields sensible interpolation even in the presence of phase space
holes. As a specific example, we apply neural conditional reweighting to the
energy response of high-energy jets, which could be used to improve the
modeling of physics objects in parametrized fast simulation packages.
