Efficient Estimation of the Dose–Response Function Under Ignorability Using Subclassification on the Covariates

Matias Cattaneo and Max Farrell

A chapter in Missing Data Methods: Cross-sectional Methods and Applications, 2011, pp 93-127 from Emerald Group Publishing Limited

Abstract: This chapter studies the large sample properties of a subclassification-based estimator of the dose–response function under ignorability. Employing standard regularity conditions, it is shown that the estimator is root-n consistent, asymptotically linear, and semiparametric efficient in large samples. A consistent estimator of the standard-error is also developed under the same assumptions. In a Monte Carlo experiment, we investigate the finite sample performance of this simple and intuitive estimator and compare it to others commonly employed in the literature.

Keywords: Missing data; treatment effects; blocking; subclassification; stratification; semiparametric efficiency (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(2011)000027a007

DOI: 10.1108/S0731-9053(2011)000027A007

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