Recent literature has underscored the critical role of treatment effect variation in estimating and understanding causal effects. This approach, however, is in contrast to much of the foundational research on causal inference. Linear models, for example, classically rely on constant treatment effect assumptions, or treatment effects defined by interaction terms. The goal of this work is to create a framework that (1) provides applied researchers with a set of practical tools and (2) clearly lays out all the relevant assumptions for assessing treatment effect variation. The authors build this from the ground up, laying out a randomization-based framework for characterizing and understanding treatment effect heterogeneity in a range of settings, including observational studies. Generally, they believe this framework allows researchers to naturally formulate and investigate questions about heterogeneity. As part of this work, the authors discuss practical trade-offs such as assessing potential loss of power from using this approach relative to conventional ones. They generally find that these costs are minimal. Their overall framework provides a toolkit of easy-to-use tools rooted in random assignment for modeling and assessing treatment effect heterogeneity. It is shown that these tools are practical, reasonably powerful, and interpretable. One table is appended.