Zobrazeno 1 - 10
of 266
pro vyhledávání: '"Didelez, Vanessa"'
Methods of causal discovery aim to identify causal structures in a data driven way. Existing algorithms are known to be unstable and sensitive to statistical errors, and are therefore rarely used with biomedical or epidemiological data. We present an
Externí odkaz:
http://arxiv.org/abs/2406.19503
Autor:
van Geloven, Nan, Keogh, Ruth H, van Amsterdam, Wouter, Cinà, Giovanni, Krijthe, Jesse H., Peek, Niels, Luijken, Kim, Magliacane, Sara, Morzywołek, Paweł, van Ommen, Thijs, Putter, Hein, Sperrin, Matthew, Wang, Junfeng, Weir, Daniala L., Didelez, Vanessa
Prediction models are increasingly proposed for guiding treatment decisions, but most fail to address the special role of treatments, leading to inappropriate use. This paper highlights the limitations of using standard prediction models for treatmen
Externí odkaz:
http://arxiv.org/abs/2402.17366
Autor:
Bang, Christine W., Didelez, Vanessa
Equivalence classes of DAGs (represented by CPDAGs) may be too large to provide useful causal information. Here, we address incorporating tiered background knowledge yielding restricted equivalence classes represented by 'tiered MPDAGs'. Tiered knowl
Externí odkaz:
http://arxiv.org/abs/2306.01638
Variable selection in linear regression settings is a much discussed problem. Best subset selection (BSS) is often considered the intuitive 'gold standard', with its use being restricted only by its NP-hard nature. Alternatives such as the least abso
Externí odkaz:
http://arxiv.org/abs/2302.12034
We consider continuous-time survival or more general event-history settings, where the aim is to infer the causal effect of a time-dependent treatment process. This is formalised as the effect on the outcome event of a (possibly hypothetical) interve
Externí odkaz:
http://arxiv.org/abs/2202.02311
Autor:
Evans, Robin J., Didelez, Vanessa
Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability di
Externí odkaz:
http://arxiv.org/abs/2109.03694
In this guide, we present how to perform constraint-based causal discovery using three popular software packages: pcalg (with add-ons tpc and micd), bnlearn, and TETRAD. We focus on how these packages can be used with observational data and in the pr
Externí odkaz:
http://arxiv.org/abs/2108.13395
Causal discovery algorithms estimate causal graphs from observational data. This can provide a valuable complement to analyses focussing on the causal relation between individual treatment-outcome pairs. Constraint-based causal discovery algorithms r
Externí odkaz:
http://arxiv.org/abs/2108.13331
Autor:
Aalen, Odd O., Stensrud, Mats J., Didelez, Vanessa, Daniel, Rhian, Røysland, Kjetil, Strohmaier, Susanne
Publikováno v:
Biometrical Journal. 2020; 62(3):532-549
We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understanding how the direct and indirect effects develop over time. Hence, im
Externí odkaz:
http://arxiv.org/abs/2011.13415
Autor:
Do, Stefanie1,2, Didelez, Vanessa2,3, Börnhorst, Claudia3, Coumans, Juul M.J.4, Reisch, Lucia A.5, Danner, Unna N.6, Russo, Paola7, Veidebaum, Toomas8, Tornaritis, Michael9, Molnár, Dénes10, Hunsberger, Monica11, De Henauw, Stefaan12, Moreno, Luis A.13, Ahrens, Wolfgang2, Hebestreit, Antje1 sec-epi@leibniz-bips.de
Publikováno v:
International Journal of Behavioral Nutrition & Physical Activity. 1/2/2024, Vol. 21 Issue 1, p1-11. 11p.