This chapter is the first of three in which we investigate how causal models can inform research design. In this chapter, we draw out the implications of the causal-model approach for clue-selection strategies, for figuring out which pieces of evidence are likely to be most informative about a question of interest. We demonstrate procedures for assessing which clues minimize expected posterior variance and how to construct an optimal decision tree for determining a dynamic clue-gathering strategy.