Assessing Heterogeneity of Treatment Effects

TL;DR

This paper develops nonparametric bounds for assessing heterogeneity in treatment effects using only marginal outcome distributions, aiding evaluation in economics where individual effects vary.

Contribution

It introduces a method to derive sharp bounds on heterogeneous treatment effects based solely on marginal distributions, without requiring full identification.

Findings

Bounds are applicable in microfinance and welfare reform contexts.

Method remains useful even when average effects are insignificant.

Supports analysis where economic theory predicts opposite effects.

Abstract

Heterogeneous treatment effects are of major interest in economics. For example, a poverty reduction measure would be best evaluated by its effects on those who would be poor in the absence of the treatment, or by the share among the poor who would increase their earnings because of the treatment. While these quantities are not identified, we derive nonparametrically sharp bounds using only the marginal distributions of the control and treated outcomes. Applications to microfinance and welfare reform demonstrate their utility even when the average treatment effects are not significant and when economic theory makes opposite predictions between heterogeneous individuals.