  In this paper, we present a generalisation of the Multilevel Monte Carlo
(MLMC) method to a setting where the level parameter is a continuous variable.
This Continuous Level Monte Carlo (CLMC) estimator provides a natural framework
in PDE applications to adapt the model hierarchy to each sample. In addition,
it can be made unbiased with respect to the expected value of the true quantity
of interest provided the quantity of interest converges sufficiently fast. The
practical implementation of the CLMC estimator is based on interpolating actual
evaluations of the quantity of interest at a finite number of resolutions. As
our new level parameter, we use the logarithm of a goal-oriented finite element
error estimator for the accuracy of the quantity of interest. We prove the
unbiasedness, as well as a complexity theorem that shows the same rate of
complexity for CLMC as for MLMC. Finally, we provide some numerical evidence to
support our theoretical results, by successfully testing CLMC on a standard PDE
test problem. The numerical experiments demonstrate clear gains for sample-wise
adaptive refinement strategies over uniform refinements.
