  The significance and influence of US Supreme Court majority opinions derive
in large part from opinions' roles as precedents for future opinions. A growing
body of literature seeks to understand what drives the use of opinions as
precedents through the study of Supreme Court case citation patterns. We raise
two limitations of existing work on Supreme Court citations. First, dyadic
citations are typically aggregated to the case level before they are analyzed.
Second, citations are treated as if they arise independently. We present a
methodology for studying citations between Supreme Court opinions at the dyadic
level, as a network, that overcomes these limitations. This methodology -- the
citation exponential random graph model, for which we provide user-friendly
software -- enables researchers to account for the effects of case
characteristics and complex forms of network dependence in citation formation.
We then analyze a network that includes all Supreme Court cases decided between
1950 and 2015. We find evidence for dependence processes, including
reciprocity, transitivity, and popularity. The dependence effects are as
substantively and statistically significant as the effects of exogenous
covariates, indicating that models of Supreme Court citation should incorporate
both the effects of case characteristics and the structure of past citations.
