Treatment effect estimation with noisy conditioning variables


Kenichi Nagasawa (Warwick)

Date & Time

11 October 2022




The Institute for Fiscal Studies
7 Ridgmount Street,

Abstract: I develop a new identification strategy for treatment effects when noisy measurements of 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 semiparametric and flexible parametric estimators of causal parameters. I illustrate empirical relevance and usefulness of my results by estimating causal effects of attending selective college on earnings.

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Venue address:

The Institute for Fiscal Studies 
7 Ridgmount Street  London WC1E 7AE

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