Before revision: We present a novel approach to nonparametric identification and consistent estimation in economic models using `proxy controls'. Our approach is particularly well-suited to the context of panel data with a fixed time-dimension but also applies in cross-sectional settings. Proxy controls are proxies for unobserved `perfect controls', where perfect controls are variables that are sufficient for the association between potential outcomes and treatments .
After revision: We present a flexible approach to the identification and estimation of causal objects in nonparametric, non-separable models with confounding. Key to our analysis is the use of `proxy controls': covariates that do not satisfy a standard `unconfoundedness' assumption but which are informative proxies for variables that do. Our methods are presented in sufficient generality that they apply to both cross-sectional and panel models .