Martin Weidner is a professor in the Department of Economics at University College London. His research interests are Econometrics, with a focus on Panel Data Models, Social Networks, Factor Models, and High-dimensional Inference.
Talk: "Bounding Treatment Effects by Pooling Limited Information across Observations" (joint work with Sokbae Lee)
Zoom details will be provided via email announcements.
Abstract: We provide novel bounds on average treatment effects (on the treated) that are valid under an unconfoundedness assumption. Our bounds are applicable if the conditioning variables do not satisfy the overlap condition, are high-dimensional, and take on a large number of different values in the observed sample. This robustness is achieved by only using limited "pooling" of information across observations. Namely, the bounds are constructed as sample averages over functions of the observed outcomes such that the contribution of each outcome only depends on the treatment status of a limited number of observations. No information pooling across observations leads to so-called "Manski bounds", while unlimited information pooling leads to standard propensity score weighting. We explore the intermediate range between these two extremes and provide corresponding inference methods.