Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Knaus, Michael"'
Autor:
Knaus, Michael C.
Estimators that weight observed outcomes to form effect estimates have a long tradition. Their outcome weights are widely used in established procedures, such as checking covariate balance, characterizing target populations, or detecting and managing
Externí odkaz:
http://arxiv.org/abs/2411.11559
Many econometrics textbooks imply that under mean independence of the regressors and the error term, the OLS parameters have a causal interpretation. We show that even when this assumption is satisfied, OLS might identify a pseudo-parameter that does
Externí odkaz:
http://arxiv.org/abs/2211.09502
Series: Arbeitspapiere des Forschungsinstituts für mittel- und osteuropäisches Wirtschaftsrecht
Externí odkaz:
http://epub.wu.ac.at/3380/1/ap103.pdf
Autor:
Knaus, Michael
(kein Abstract vorhanden)
Series: Arbeitspapiere des Forschungsinstituts für mittel- und osteuropäisches Wirtschaftsrecht
Series: Arbeitspapiere des Forschungsinstituts für mittel- und osteuropäisches Wirtschaftsrecht
Externí odkaz:
http://epub.wu.ac.at/3379/1/ap085.pdf
Autor:
Knaus, Michael
(kein Abstract vorhanden)
Series: Arbeitspapiere des Forschungsinstituts für mittel- und osteuropäisches Wirtschaftsrecht
Series: Arbeitspapiere des Forschungsinstituts für mittel- und osteuropäisches Wirtschaftsrecht
Externí odkaz:
http://epub.wu.ac.at/3377/1/ap076.pdf
Autor:
Knaus, Michael
Series: Arbeitspapiere des Forschungsinstituts für mittel- und osteuropäisches Wirtschaftsrecht
Externí odkaz:
http://epub.wu.ac.at/3375/1/ap055.pdf
Autor:
Heiler, Phillip, Knaus, Michael C.
Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that
Externí odkaz:
http://arxiv.org/abs/2110.01427
Autor:
Knaus, Michael C.
This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variab
Externí odkaz:
http://arxiv.org/abs/2003.03191
Publikováno v:
Econometrics Journal (2021), volume 24, pp.134-161
We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes
Externí odkaz:
http://arxiv.org/abs/1810.13237
Autor:
Knaus, Michael C.
This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study propo
Externí odkaz:
http://arxiv.org/abs/1805.10300