  This paper concerns the use of a particular class of determinantal point
processes (DPP), a class of repulsive spatial point processes, for Monte Carlo
integration. Let $d\ge 1$, $I\subseteq \overline d=\{1,\dots,d\}$ with
$\iota=|I|$. Using a single set of $N$ quadrature points $\{u_1,\dots,u_N\}$
defined, once for all, in dimension $d$ from the realization of the DPP model,
we investigate "minimal" assumptions on the integrand in order to obtain
unbiased Monte Carlo estimates of $\mu(f_I)=\int_{[0,1]^\iota} f_I(u)
\mathrm{d} u$ for any known $\iota$-dimensional integrable function on
$[0,1]^\iota$. In particular, we show that the resulting estimator has variance
with order $N^{-1-(2s\wedge 1)/d}$ when the integrand belongs to some Sobolev
space with regularity $s > 0$. When $s>1/2$ (which includes a large class of
non-differentiable functions), the variance is asymptotically explicit and the
estimator is shown to satisfy a Central Limit Theorem.
