  LHC analyses directly comparing data and simulated events bear the danger of
using first-principle predictions only as a black-box part of event simulation.
We show how simulations, for instance, of detector effects can instead be
inverted using generative networks. This allows us to reconstruct parton level
information from measured events. Our results illustrate how, in general, fully
conditional generative networks can statistically invert Monte Carlo
simulations. As a technical by-product we show how a maximum mean discrepancy
loss can be staggered or cooled.
