This work considers the problem of approximating fixed predicate constraint satisfaction problems (MAX k-CSP(P)). We show that if the set of assignments accepted by $P$ contains the support of a balanced pairwise independent distribution over the domain of the inputs, then such a problem on $n$ variables cannot be approximated better than the trivial (random) approximation, even using $\Omega(n)$ levels of the Sherali-Adams LP hierarchy.

It was recently shown~\cite{AM08} that under the Unique Game Conjecture, CSPs for predicates satisfying this condition cannot be approximated better than the trivial approximation. Our results can be viewed as an unconditional analogue of this result in the restricted computational model defined by the Sherali-Adams hierarchy. We also introduce a new generalization of techniques to define consistent ``local distributions" over partial assignments to variables in the problem, which is often the crux of proving lower bounds for such hierarchies.