Assessing Heterogeneity of Treatment Effects

Abstract: Treatment effect heterogeneity is of major interest in economics, but its assessment is often hindered by the fundamental lack of identification of the individual treatment effects. For example, we may want to assess the effect of a poverty reduction measure at different levels of poverty, but the causal effects on wealth at different wealth levels are not identified. Or, we may be interested in the proportion of workers who benefit from the minimum wage increase, but the proportion is not identified in the absence of counterfactuals. This paper derives bounds useful in such situations, which only depend on the marginal distributions of the outcomes. The bounds are nonparametrically sharp, making clear the maximum extent to which the data can speak about the heterogeneity of the treatment effects. An application to microfinance shows that the bounds can be informative even when the average treatment effects are not significant. Another application to the welfare reform identifies a nonnegligible portion of workers who increased and decreased working hours due to the reform.