Randomized controlled experiments are often described as the most reliable tool available to scientists
for discovering causal relationships among quantities of interest. However, it is often unclear
how many and which different experiments are needed to identify the full (possibly cyclic) causal
structure among some given (possibly causally insufficient) set of variables. Recent results in the
causal discovery literature have explored various identifiability criteria that depend on the assumptions
one is able to make about the underlying causal process, but these criteria are not directly
constructive for selecting the optimal set of experiments. Fortunately, many of the needed constructions
already exist in the combinatorics literature, albeit under terminology which is unfamiliar to
most of the causal discovery community. In this paper we translate the theoretical results and apply
them to the concrete problem of experiment selection. For a variety of settings we give explicit
constructions of the optimal set of experiments and adapt some of the general combinatorics results
to answer questions relating to the problem of experiment selection