  We study causal inference in a setting in which units consisting of pairs of
individuals (such as married couples) are assigned randomly to one of four
categories: a treatment targeted at pair member A, a potentially different
treatment targeted at pair member B, joint treatment, or no treatment. The
setup includes the important special case in which the pair members are the
same individual targeted by two different treatments A and B. Allowing for
endogenous non-compliance, including coordinated treatment takeup, as well as
interference across treatments, we derive the causal interpretation of various
instrumental variable estimands using weaker monotonicity conditions than in
the literature. In general, coordinated treatment takeup makes it difficult to
separate treatment interaction from treatment effect heterogeneity. We provide
auxiliary conditions and various bounding strategies that may help zero in on
causally interesting parameters. As an empirical illustration, we apply our
results to a program randomly offering two different treatments, namely
tutoring and financial incentives, to first year college students, in order to
assess the treatments' effects on academic performance.
