Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Leonard Henckel"'
Publikováno v:
Henckel, L, Perković, E & Maathuis, M H 2022, ' Graphical criteria for efficient total effect estimation via adjustment in causal linear models ', Journal of the Royal Statistical Society. Series B: Statistical Methodology, vol. 84, no. 2, pp. 579-599 . https://doi.org/10.1111/rssb.12451
Journal of the Royal Statistical Society. Series B, Statistical Methodology, 84 (2)
Journal of the Royal Statistical Society. Series B, Statistical Methodology, 84 (2)
Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different
Publikováno v:
Journal of Machine Learning Research, 21
Journal of machine learning research, 21(246):1-45
Scopus-Elsevier
Journal of machine learning research, 21(246):1-45
Scopus-Elsevier
We consider estimation of a total causal effect from observational data via covariate adjustment. Ideally, adjustment sets are selected based on a given causal graph, reflecting knowledge of the underlying causal structure. Valid adjustment sets are,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0dfcfa907a86093ae2ad8d8113dc4291
https://hdl.handle.net/20.500.11850/470140
https://hdl.handle.net/20.500.11850/470140
Autor:
Zehao Su, Leonard Henckel
Publikováno v:
University of Copenhagen
Su, Z & Henckel, L 2022, A robustness test for estimating total effects with covariate adjustment . in Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence . 1 edn, PMLR, Proceedings of Machine Learning Research, vol. 180, pp. 1886-1895, 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), Eindhoven, Netherlands, 01/08/2022 . < https://proceedings.mlr.press/v180/su22a.html >
Su, Z & Henckel, L 2022, A robustness test for estimating total effects with covariate adjustment . in Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence . 1 edn, PMLR, Proceedings of Machine Learning Research, vol. 180, pp. 1886-1895, 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), Eindhoven, Netherlands, 01/08/2022 . < https://proceedings.mlr.press/v180/su22a.html >
Suppose we want to estimate a total effect with covariate adjustment in a linear structural equation model. We have a causal graph to decide what covariates to adjust for, but are uncertain about the graph. Here, we propose a testing procedure, that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff7824cfc9889c474f7ee0bd8778f878
https://curis.ku.dk/portal/en/publications/a-robustness-test-for-estimating-total-effects-with-covariate-adjustment(212241fd-8664-4124-ba6c-dbe0aa57daab).html
https://curis.ku.dk/portal/en/publications/a-robustness-test-for-estimating-total-effects-with-covariate-adjustment(212241fd-8664-4124-ba6c-dbe0aa57daab).html