Instrumented Common Confounding (ICC) identifies causal effects with instruments, which are exogenous conditional on some unobserved common confounders. Suitable examples of this setting are various identification problems in the social sciences, dynamic panels, and problems with multiple endogenous confounders. The ICC identifying assumptions are closely related to those in mixture models, negative control and IV. We prove point identification with outcome model and alternatively first stage restrictions, and present the causal effect of education on income as a motivating example.

In this paper written in about two days for the Econometric Game 2021, we use employ Generalised Random Forests to estimate a local average partial effect that comes closest to a causal effect of Airbnb on house prices. The estimated average treatment effects show a nuanced picture of the causal effect of Airbnb presence on local housing demand.