  A confidence distribution is a complete tool for making frequentist inference
for a parameter of interest $\psi$ based on an assumed parametric model.
Indeed, it allows to reach point estimates, to assess their precision, to set
up tests along with measures of evidence for statements of the type "$\psi >
\psi_0$" or "$\psi_1 \leq \psi \leq \psi_2$", to derive confidence intervals,
comparing the parameter of interest with other parameters from other studies,
etc.
  The aim of this contribution is to discuss robust confidence distributions
derived from unbiased $M-$estimating functions, which provide robust inference
for $\psi$ when the assumed distribution is just an approximate parametric
model or in the presence of deviant values in the observed data. Paralleling
likelihood-based results and extending results available for robust scoring
rules, we first illustrate how robust confidence distributions can be derived
from the asymptotic theory of robust pivotal quantities. Then, we discuss the
derivation of robust confidence distributions via simulation methods. An
application and a simulation study are illustrated in the context of
non-inferiority testing, in which null hypotheses of the form $H_0: \psi \leq
\psi_0$ are of interest.
