  A bipartite experiment consists of one set of units being assigned treatments
and another set of units for which we measure outcomes. The two sets of units
are connected by a bipartite graph, governing how the treated units can affect
the outcome units. In this paper, we consider estimation of the average total
treatment effect in the bipartite experimental framework under a linear
exposure-response model. We introduce the Exposure Reweighted Linear (ERL)
estimator, and show that the estimator is unbiased, consistent and
asymptotically normal, provided that the bipartite graph is sufficiently
sparse. To facilitate inference, we introduce an unbiased and consistent
estimator of the variance of the ERL point estimator. In addition, we introduce
a cluster-based design, Exposure-Design, that uses heuristics to increase the
precision of the ERL estimator by realizing a desirable exposure distribution.
