  Bipartite experiments are a recent object of study in causal inference,
whereby treatment is applied to one set of units and outcomes of interest are
measured on a different set of units. These experiments are particularly useful
in settings where strong interference effects occur between units of a
bipartite graph. In market experiments for example, assigning treatment at the
seller-level and measuring outcomes at the buyer-level (or vice-versa) may lead
to causal models that better account for the interference that naturally occurs
between buyers and sellers. While bipartite experiments have been shown to
improve the estimation of causal effects in certain settings, the analysis must
be done carefully so as to not introduce unnecessary bias. We leverage the
generalized propensity score literature to show that we can obtain unbiased
estimates of causal effects for bipartite experiments under a standard set of
assumptions. We also discuss the construction of confidence sets with proper
coverage probabilities. We evaluate these methods using a bipartite graph from
a publicly available dataset studied in previous work on bipartite experiments,
showing through simulations a significant bias reduction and improved coverage.
