Bounding, an accessible method for estimating principal causal effects, examined and explained
Luke Miratrix, Jane Furey, Avi Feller, Todd Grindal, and Lindsay Page
Estimating program effects for subgroups is hard. Estimating effects for types of people who exist in theory, but whom we can’t always identify in practice (i.e., latent subgroups) is harder. These challenges arise often, with noncompliance being a primary example. Another is estimating effects on groups defined by “counterfactual experience,” i.e., by what opportunities would have been available absent treatment access. This paper tackles this difficult problem. We find that if one can predict, with some accuracy, latent subgroup membership, then bounding is a nice evaluation approach, relying on weak assumptions. This is in contrast to many alternatives that are tricky, often unstable, and/or rely on heroic assumptions.
What are latent subgroups again?
