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We derive general, yet simple, sharp bounds on the size of the omitted variable bias for a broad class of causal … bound on the bias depends only on the additional variation that the latent variables create both in the outcome and in the … (in explaining treatment and outcome variation) are sufficient to place overall bounds on the size of the bias. Finally …
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have smaller bias that is flatter as a function of first step smoothing leading to improved small sample properties. Series …
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There are many interesting and widely used estimators of a functional with finite semi-parametric variance bound that depend on nonparametric estimators of nuisance func-tions. We use cross-fitting to construct such estimators with fast remainder rates. We give cross-fit doubly robust...
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bias and so are important when the first step is machine learning. We derive LR moment conditions for dynamic discrete …
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