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pro vyhledávání: '"Laffers, Lukas"'
We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on the test by Huber and
Externí odkaz:
http://arxiv.org/abs/2406.13826
We consider evaluating the causal effects of dynamic treatments, i.e. of multiple treatment sequences in various periods, based on double machine learning to control for observed, time-varying covariates in a data-driven way under a selection-on-obse
Externí odkaz:
http://arxiv.org/abs/2012.00370
This paper considers the evaluation of discretely distributed treatments when outcomes are only observed for a subpopulation due to sample selection or outcome attrition. For identification, we combine a selection-on-observables assumption for treatm
Externí odkaz:
http://arxiv.org/abs/2012.00745
This paper combines causal mediation analysis with double machine learning to control for observed confounders in a data-driven way under a selection-on-observables assumption in a high-dimensional setting. We consider the average indirect effect of
Externí odkaz:
http://arxiv.org/abs/2002.12710
Autor:
Huber, Martin, Lafférs, Lukáš
Causal mediation analysis aims at disentangling a treatment effect into an indirect mechanism operating through an intermediate outcome or mediator, as well as the direct effect of the treatment on the outcome of interest. However, the evaluation of
Externí odkaz:
http://arxiv.org/abs/2002.05253
Akademický článek
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We formulate a baseline model for the net-proton number fluctuations in ultrarelativistic heavy-ion collisions. Our model includes total baryon number conservation, fluctuations of the participating nucleon number, limited acceptance, and a possibili
Externí odkaz:
http://arxiv.org/abs/1903.11494
Akademický článek
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Publikováno v:
Journal of Applied Econometrics, 2017 Jan 01. 32(1), 56-79.
Externí odkaz:
https://www.jstor.org/stable/26609692
