Nonparametric Covariate Adjustment in Finite Samples
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In this chapter the finite-sample properties of various estimators of the covariate-adjusted mean (2.25) are investigated. Estimates of (2.25) are essential ingredients for policy evaluation under the control-for-confounding-variables and under the difference-in-difference identification approaches (discussed in Section 2.1.4). Hence precise estimation of covariate-adjusted means is of importance, particularly if average treatment effects are analyzed for smaller subpopulations.1 In addition, estimation of covariate-adjusted means is also central for deriving individually optimal treatment choices in Chapter 4.
KeywordsPropensity Score Optimal Bandwidth Estimate Propensity Score Kernel Match Bandwidth Selector
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- 2.Some simulations were also carried out with local quadratic and local cubic matching and with a local linear variant of Hall, Park, and Turlach (1998). These estimators, however, did not perform well in small samples.Google Scholar