Sven Klaassen
Biographic Data
| ID | 8920198 |
|---|---|
| NAME | Sven Klaassen |
| GIVEN NAMES | Sven |
| FAMILY NAME | Klaassen |
| SIGNATURE | KLAASSEN S |
| AFFILIATIONS | Universität Hamburg |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Instrument Validity Tests With Causal Forests
Assumptions that are sufficient to identify local average treatment effects (LATEs) generate necessary conditions that allow instrument validity to be refuted. The degree to which instrument validity is violated, however, probably varies across subpopulations. In this article, we use causal forests to search and test for such local violations of the LATE assumptions in a data-driven way. Unlike previous instrument validity tests, our procedure is…
Transformation Models in High Dimensions
Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze transformation models in a high-dimensional setting, where the set of potential covariates is large. We propose an estimator for the transformation parameter and we show that it is asymptotically normally…
No prominent works on this page.
Instrument Validity Tests With Causal Forests
Assumptions that are sufficient to identify local average treatment effects (LATEs) generate necessary conditions that allow instrument validity to be refuted. The degree to which instrument validity is violated, however, probably varies across subpopulations. In this article, we use causal forests to search and test for such local violations of the LATE assumptions in a data-driven way. Unlike previous instrument validity tests, our procedure is…
Transformation Models in High Dimensions
Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze transformation models in a high-dimensional setting, where the set of potential covariates is large. We propose an estimator for the transformation parameter and we show that it is asymptotically normally…
Computer Science (2 works) · Econometrics (2 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Advanced Causal Inference Techniques (1 work) · Advanced Statistical Methods and Models (1 work) · Applied Mathematics (1 work) · Covariate (1 work) · Ecology (1 work)