  In randomized experiments, the classic stable unit treatment value assumption
(SUTVA) states that the outcome for one experimental unit does not depend on
the treatment assigned to other units. However, the SUTVA assumption is often
violated in applications such as online marketplaces and social networks where
units interfere with each other. We consider the estimation of the average
treatment effect in a network interference model using a mixed randomization
design that combines two commonly used experimental methods: Bernoulli
randomized design, where treatment is independently assigned for each
individual unit, and cluster-based design, where treatment is assigned at an
aggregate level. Essentially, a mixed randomization experiment runs these two
designs simultaneously, allowing it to better measure the effect of network
interference. We propose an unbiased estimator for the average treatment effect
under the mixed design and show the variance of the estimator is bounded by
$O({d^2}n^{-1}p^{-1})$ where $d$ is the maximum degree of the network, $n$ is
the network size, and $p$ is the probability of treatment. We also establish a
lower bound of $\Omega(d^{1.5}n^{-1}p^{-1})$ for the variance of any mixed
design. For a family of sparse networks characterized by a growth constant
$\kappa \leq d$, we improve the upper bound to $O({\kappa^7 d}n^{-1}p^{-1})$.
Furthermore, when interference weights on the edges of the network are unknown,
we propose a weight-invariant design that achieves a variance bound of
$O({d^3}n^{-1}p^{-1})$.
