On the Identifying Power of Generalized Monotonicity for Average Treatment Effects

TL;DR

This paper extends the understanding of the limitations of generalized monotonicity in identifying average treatment effects, showing it offers no additional identification power beyond instrument exogeneity unless stronger restrictions are imposed.

Contribution

It demonstrates that generalized monotonicity does not improve identification of average treatment effects unless additional restrictions that alter potential outcomes are considered.

Findings

Generalized monotonicity offers no extra identification power beyond exogeneity.

Stronger restrictions than generalized monotonicity can provide additional identification.

Many existing models imply generalized monotonicity, limiting their identification capabilities.

Abstract

In the context of a binary outcome, treatment, and instrument, Balke and Pearl (1993, 1997) es- tablish that the monotonicity condition of Imbens and Angrist (1994) has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This paper shows that this phenomenon holds in a broader setting with a multi-valued outcome, treatment, and instrument, under an extension of the monotonicity condition that we refer to as generalized monotonicity. We further show that this phenomenon holds for any restriction on treatment response that is stronger than generalized monotonicity provided that these stronger restrictions do not restrict potential outcomes. Importantly, many models of potential treatments previously considered in the literature imply generalized monotonic- ity, including the types of monotonicity restrictions considered by Kline and Walters (2016), Kirkeboen et al. (2016), and Heckman and Pinto (2018), and the restriction that treatment selection is determined by particular classes of additive random utility models. We show through a series of examples that restrictions on potential treatments can provide identifying power beyond instrument exogeneity for av- erage potential outcomes and average treatment effects when the restrictions imply that the generalized monotonicity condition is violated. In this way, our results shed light on the types of restrictions required for help in identifying average potential outcomes and average treatment effects.