Detecting Latent Heterogeneity

What is it about?

We address the task of determining, from statistical averages alone, whether a population under study consists of several subpopulations, unknown to the investigator, each responding to a given treatment markedly differently. We show that such determination is feasible in three cases: (1) randomized trials with binary treatments, (2) models where treatment effects can be identified by adjustment for covariates, and (3) models in which treatment effects can be identified by mediating instruments. In each of these cases, we provide an explicit condition which, if confirmed empirically, proves that treatment effect is not uniform but varies appreciably across individuals.

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