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.
Cattaneo, M.D. and Farrell, M.H. (2011), "Efficient Estimation of the Dose–Response Function Under Ignorability Using Subclassification on the Covariates", Drukker, D.M. (Ed.) Missing Data Methods: Cross-sectional Methods and Applications (Advances in Econometrics, Vol. 27 Part 1), Emerald Group Publishing Limited, Bingley, pp. 93-127. https://doi.org/10.1108/S0731-9053(2011)000027A007Download as .RIS
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