  In this work, we derive an Edgeworth expansion for the Bernoulli weighted
mean $\hat{\mu} = \frac{\sum_{i=1}^n Y_i T_i}{\sum_{i=1}^n T_i}$ in the case
where $Y_1, \dots, Y_n$ are i.i.d. non semi-lattice random variables and $T_1,
\dots, T_n$ are Bernoulli distributed random variables with parameter $p$. We
also define the notion of a semi-lattice distribution, which gives a more
geometrical equivalence to the classical Cram\'er's condition in dimensions
bigger than 1. Our result provides a first step into the generalization of
classical Edgeworth expansion theorems for random vectors that contain both
semi-lattice and non semi-lattice variables, in order to prove consistency of
bootstrap methods in more realistic setups, for instance in the use case of
online AB testing.
