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Testing the identification of causal effects in observational data
This study demonstrates the existence of a testable condition for the identification of the causal effect of a treatment on an outcome in observational data, which relies on two sets of variables: observed covariates to be controlled for and a suspected instrument.
Estimation and Uniform Inference in Sparse High-Dimensional Additive Models
We develop a novel method to construct uniformly valid confidence bands for a nonparametric component $f_1$ in the sparse additive model $Y=f_1(X_1)+\ldots + f_p(X_p) + \varepsilon$ in a high-dimensional setting.
Estimation and Inference of Treatment Effects with $L_2$-Boosting in High-Dimensional Settings
In this paper, we provide results for valid inference after post- or orthogonal $L_2$-Boosting is used for variable selection.
Transformation Models in High-Dimensions
Transformation models are a very important tool for applied statisticians and econometricians.
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