  Approximate Bayesian computation (ABC) is a popular likelihood-free inference
method for models with intractable likelihood functions. As ABC methods usually
rely on comparing summary statistics of observed and simulated data, the choice
of the statistics is crucial. This choice involves a trade-off between loss of
information and dimensionality reduction, and is often determined based on
domain knowledge. However, handcrafting and selecting suitable statistics is a
laborious task involving multiple trial-and-error steps. In this work, we
introduce an active learning method for ABC statistics selection which reduces
the domain expert's work considerably. By involving the experts, we are able to
handle misspecified models, unlike the existing dimension reduction methods.
Moreover, empirical results show better posterior estimates than with existing
methods, when the simulation budget is limited.
