Conjoint experiments are fast becoming one of the dominant experimental methods within the social sciences. Despite several scholars advancing novel ways to model heterogeneity within this type of design, the relationship between these new quantities and the conjoint design is underdeveloped. In this note, we clarify how conjoint heterogeneity can be construed as a set of nested, causal parameters that correspond to the levels of the conjoint design. We then use this framework to propose a new estimation strategy that allows researchers to evaluate treatment effect heterogeneity and which exhibits good statistical properties. Replicating two conjoint experiments, we first demonstrate our theoretical argument, and then show how this method helps uncover interesting heterogeneity. To accompany this paper, we provide new a R package, cjbart, that allows researchers to model heterogeneity in their experimental conjoint data.