Set-Valued Control Functions
Abstract
The control function approach allows the researcher to identify various causal effectsof interest. While powerful, it requires a strong invertibility assumption, which limits
its applicability. This paper expands the scope of the nonparametric control function
approach by allowing the control function to be set-valued and derive sharp bounds
on structural parameters. The proposed generalization accommodates a wide range of
selection processes involving discrete endogenous variables, random coefficients, treatment
selections with interference, and dynamic treatment selections.