
On IPWbased estimation of conditional average treatment effect
The research in this paper gives a systematic investigation on the asymp...
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Nonparametric Estimation of Conditional Expectation with Auxiliary Information and Dimension Reduction
Nonparametric estimation of the conditional expectation E(Y  U) of an o...
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Doubly robust estimation of average treatment effect revisited
The research described herewith is to revisit the classical doubly robu...
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Balancing Out Regression Error: Efficient Treatment Effect Estimation without Smooth Propensities
There has been a recent surge of interest in doubly robust approaches to...
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Collaborative double robustness using the escore
Estimation of causal parameters from observational data requires complet...
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A Semiparametric Approach to Model Effect Modification
One fundamental statistical question for research areas such as precisio...
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Doubly robust estimation for conditional treatment effect: a study on asymptotics
In this paper, we apply doubly robust approach to estimate, when some co...
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Outcome regressionbased estimation of conditional average treatment effect
The research is about a systematic investigation on the following issues. First, we construct different outcome regressionbased estimators for conditional average treatment effect under, respectively, true (oracle), parametric, nonparametric and semiparametric dimension reduction structure. Second, according to the corresponding asymptotic variance functions, we answer the following questions when supposing the models are correctly specified: what is the asymptotic efficiency ranking about the four estimators in general? how is the efficiency related to the affiliation of the given covariates in the set of arguments of the regression functions? what do the roles of bandwidth and kernel function selections play for the estimation efficiency; and in which scenarios should the estimator under semiparametric dimension reduction regression structure be used in practice? As a byproduct, the results show that any outcome regressionbased estimation should be asymptotically more efficient than any inverse probability weightingbased estimation. All these results give a relatively complete picture of the outcome regressionbased estimation such that the theoretical conclusions could provide guidance for practical use when more than one estimations can be applied to the same problem. Several simulation studies are conducted to examine the performances of these estimators in finite sample cases and a real dataset is analyzed for illustration.
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