Estimating conditional average treatment effects with heteroscedasticity by model averaging and matching
Economics Letters(2024)
摘要
We propose a model averaging approach, combined with a partition and matching method to estimate the conditional average treatment effects under heteroskedastic error settings. The proposed approach has asymptotic optimality and consistency of weights and estimator. Numerical studies show that our method has good finite-sample performances.
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关键词
Asymptotic optimality,Cross validation,Heteroscedasticity,Model averaging,Treatment effects
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