Christian Tien is a final year PhD student in Economics at the University of Cambridge. His research interests include causal inference and machine learning. He likes to come up with creative approaches to interesting economic identification problems. His research in the intersection of instrumental variables and proximal learning received the Tom Ten Have Award honorable mention at the Americal Causal Inference Conference 2023.
PhD in Economics, 2023
University of Cambridge
MPhil in Economic Research, 2019
University of Cambridge
BA in Economics, 2018
University of Cambridge
Finding excluded (and exogenous) instruments is hard. We consider the situation where instruments are excluded only conditional on some unobserved common confounders, for which relevant proxies exist. Using insights from proximal learning, we can identify exogenous variation in the instruments to then identify a causal effect of a treament on an outcome. All our relevance assumptions are testable, while as usual in IV, the assumption of exclusion conditional on unobservables is not (up to specification tests). Importantly, exclusion conditional on unobservables for which proxies exist may be a weaker assumption than exclusion conditional on observables only.
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.