In this talk, we discuss extending causal inference techniques to settings where the outcome of interest is an element of a metric space. We begin by reviewing a traditional causal inference setting in which outcomes are real-valued. We then define a novel set of causal parameters applicable when the outcomes lie in a metric space, and explore how they differ from familiar parameters such as the Average Treatment Effect. Finally, we will outline estimation strategies based on inverse probability weighting and kernel regression, and demonstrate their performance in simulations.