277 related articles for article (PubMed ID: 32294233)
1. Bayesian inference of causal effects from observational data in Gaussian graphical models.
Castelletti F; Consonni G
Biometrics; 2021 Mar; 77(1):136-149. PubMed ID: 32294233
[TBL] [Abstract][Full Text] [Related]
2. Objective Bayesian search of Gaussian directed acyclic graphical models for ordered variables with non-local priors.
Altomare D; Consonni G; La Rocca L
Biometrics; 2013 Jun; 69(2):478-87. PubMed ID: 23560520
[TBL] [Abstract][Full Text] [Related]
3. Bayesian learning of multiple directed networks from observational data.
Castelletti F; La Rocca L; Peluso S; Stingo FC; Consonni G
Stat Med; 2020 Dec; 39(30):4745-4766. PubMed ID: 32969059
[TBL] [Abstract][Full Text] [Related]
4. Functional Bayesian networks for discovering causality from multivariate functional data.
Zhou F; He K; Wang K; Xu Y; Ni Y
Biometrics; 2023 Dec; 79(4):3279-3293. PubMed ID: 37635676
[TBL] [Abstract][Full Text] [Related]
5. Causal inference in cumulative risk assessment: The roles of directed acyclic graphs.
Brewer LE; Wright JM; Rice G; Neas L; Teuschler L
Environ Int; 2017 May; 102():30-41. PubMed ID: 27988137
[TBL] [Abstract][Full Text] [Related]
6. Learning an L1-regularized Gaussian Bayesian network in the equivalence class space.
Vidaurre D; Bielza C; Larrañaga P
IEEE Trans Syst Man Cybern B Cybern; 2010 Oct; 40(5):1231-42. PubMed ID: 20083459
[TBL] [Abstract][Full Text] [Related]
7. Inferring dynamic genetic networks with low order independencies.
Lèbre S
Stat Appl Genet Mol Biol; 2009; 8():Article 9. PubMed ID: 19222392
[TBL] [Abstract][Full Text] [Related]
8. Bayesian identification of structural coefficients in causal models and the causal false-positive risk of confounders and colliders in linear Markovian models.
Kelter R
BMC Med Res Methodol; 2022 Feb; 22(1):58. PubMed ID: 35220960
[TBL] [Abstract][Full Text] [Related]
9. Individualized causal discovery with latent trajectory embedded Bayesian networks.
Zhou F; He K; Ni Y
Biometrics; 2023 Dec; 79(4):3191-3202. PubMed ID: 36807295
[TBL] [Abstract][Full Text] [Related]
10. Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms.
Aalen OO; Røysland K; Gran JM; Kouyos R; Lange T
Stat Methods Med Res; 2016 Oct; 25(5):2294-2314. PubMed ID: 24463886
[TBL] [Abstract][Full Text] [Related]
11. Joint estimation of causal effects from observational and intervention gene expression data.
Rau A; Jaffrézic F; Nuel G
BMC Syst Biol; 2013 Oct; 7():111. PubMed ID: 24172639
[TBL] [Abstract][Full Text] [Related]
12. Causal inference in biology networks with integrated belief propagation.
Chang R; Karr JR; Schadt EE
Pac Symp Biocomput; 2015; ():359-70. PubMed ID: 25592596
[TBL] [Abstract][Full Text] [Related]
13. [Causal Inference in Medicine Part II. Directed acyclic graphs--a useful method for confounder selection, categorization of potential biases, and hypothesis specification].
Suzuki E; Komatsu H; Yorifuji T; Yamamoto E; Doi H; Tsuda T
Nihon Eiseigaku Zasshi; 2009 Sep; 64(4):796-805. PubMed ID: 19797848
[TBL] [Abstract][Full Text] [Related]
14. A tutorial on bayesian networks for psychopathology researchers.
Briganti G; Scutari M; McNally RJ
Psychol Methods; 2023 Aug; 28(4):947-961. PubMed ID: 35113632
[TBL] [Abstract][Full Text] [Related]
15. On joint estimation of Gaussian graphical models for spatial and temporal data.
Lin Z; Wang T; Yang C; Zhao H
Biometrics; 2017 Sep; 73(3):769-779. PubMed ID: 28099997
[TBL] [Abstract][Full Text] [Related]
16. Bayesian causal inference for observational studies with missingness in covariates and outcomes.
Zang H; Kim HJ; Huang B; Szczesniak R
Biometrics; 2023 Dec; 79(4):3624-3636. PubMed ID: 37553770
[TBL] [Abstract][Full Text] [Related]
17. Mixed Bayesian networks: a mixture of Gaussian distributions.
Chevrolat JP; Rutigliano F; Golmard JL
Methods Inf Med; 1994 Dec; 33(5):535-42. PubMed ID: 7869953
[TBL] [Abstract][Full Text] [Related]
18. Estimating causal effects from panel data with dynamic multivariate panel models.
Helske J; Tikka S
Adv Life Course Res; 2024 Jun; 60():100617. PubMed ID: 38759570
[TBL] [Abstract][Full Text] [Related]
19. DAG With Omitted Objects Displayed (DAGWOOD): a framework for revealing causal assumptions in DAGs.
Haber NA; Wood ME; Wieten S; Breskin A
Ann Epidemiol; 2022 Apr; 68():64-71. PubMed ID: 35124197
[TBL] [Abstract][Full Text] [Related]
20. Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments.
Teramoto R; Saito C; Funahashi S
BMC Bioinformatics; 2014 Jun; 15():228. PubMed ID: 24980787
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
