  Precision measurements at the LHC often require analyzing high-dimensional
event data for subtle kinematic signatures, which is challenging for
established analysis methods. Recently, a powerful family of multivariate
inference techniques that leverage both matrix element information and machine
learning has been developed. This approach neither requires the reduction of
high-dimensional data to summary statistics nor any simplifications to the
underlying physics or detector response. In this paper we introduce MadMiner, a
Python module that streamlines the steps involved in this procedure. Wrapping
around MadGraph5_aMC and Pythia 8, it supports almost any physics process and
model. To aid phenomenological studies, the tool also wraps around Delphes 3,
though it is extendable to a full Geant4-based detector simulation. We
demonstrate the use of MadMiner in an example analysis of dimension-six
operators in ttH production, finding that the new techniques substantially
increase the sensitivity to new physics.
