Скачать или смотреть Guido Imbens: Identification of nonparametric factor models for average treatment effects

Subscribe to the channel to get notified when we release a new video.
Like the video to tell YouTube that you want more content like this on your feed.
See our website for future seminars: https://sites.google.com/view/ocis/home
Tuesday, Mar 25, 2025: Guido Imbens (Stanford University)
Title: Identification of nonparametric factor models for average treatment effects
Discussant: Bryan Graham (UC Berkeley)
Abstract: There is a growing literature on methods for estimating causal effects in settings with panel or longitudinal data using two-way-fixed-effect, linear factor, and synthetic control methods. These methods attempt to adjust for unobserved differences between units as well as unobserved differences over time. Many of these methods partly rely on functional form assumptions to allow for such adjustments. Here we propose a set up that does not involve functional form assumptions. We show that by matching units on a similarity distance we can find units that are comparable in terms of expected outcomes and use those units to consistently estimate average causal effects under a matrix version of standard ignorability conditions.