The title of talk is "Treatment effect estimation with noisy conditioning variables" and the abstract can be found below.

 In this paper, I develop a new identification strategy for treatment effects when proxy variables for unobserved confounding factors are available. I use proxy variables to construct a random variable conditional on which treatment variables become exogenous. The key idea is that, under appropriate conditions, there exists a one-to-one mapping between the distribution of unobserved confounding factors and the distribution of proxies. To ensure sufficient variation in the constructed control variable, I use an additional variable, termed excluded variable, which satisfies certain exclusion restrictions and relevance conditions. I establish asymptotic distributional results for flexible parametric and nonparametric estimators of the average structural function. I illustrate empirical relevance and usefulness of my results by extending the identification strategy of Dale and Krueger (2002, QJE).