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Precision measurements at the LHC often require analyzing high-dimensionalevent data for subtle kinematic signatures, which is challenging forestablished analysis methods. Recently, a powerful family of multivariateinference techniques that leverage both matrix element information and machinelearning has been developed. This approach neither requires the reduction ofhigh-dimensional data to summary statistics nor any simplifications to theunderlying physics or detector response. In this paper we introduce MadMiner, aPython module that streamlines the steps involved in this procedure. Wrappingaround MadGraph5_aMC and Pythia 8, it supports almost any physics process andmodel. To aid phenomenological studies, the tool also wraps around Delphes 3,though it is extendable to a full Geant4-based detector simulation. Wedemonstrate the use of MadMiner in an example analysis of dimension-sixoperators in ttH production, finding that the new techniques substantiallyincrease the sensitivity to new physics.

MadMiner: Machine learning-based inference for particle physics