Literature DB >> 29096050

Discussion of "Data-driven confounder selection via Markov and Bayesian networks" by Häggström.

Thomas S Richardson1, James M Robins2, Linbo Wang2.   

Abstract

Entities:  

Keywords:  Causal inference; Generalized back-door criterion; M-bias; Partial ancestral graph; Variable selection

Mesh：

Year:  2017        PMID： 29096050      PMCID： PMC5932283          DOI： 10.1111/biom.12784

Source DB:  PubMed          Journal:  Biometrics        ISSN： 0006-341X            Impact factor:   2.571


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  5 in total

1.  Collaborative double robust targeted maximum likelihood estimation.

Authors:  Mark J van der Laan; Susan Gruber
Journal:  Int J Biostat       Date:  2010-05-17       Impact factor: 0.968

2.  Outcome-adaptive lasso: Variable selection for causal inference.

Authors:  Susan M Shortreed; Ashkan Ertefaie
Journal:  Biometrics       Date:  2017-03-08       Impact factor: 2.571

3.  Data-driven confounder selection via Markov and Bayesian networks.

Authors:  Jenny Häggström
Journal:  Biometrics       Date:  2017-11-02       Impact factor: 2.571

4.  A new criterion for confounder selection.

Authors:  Tyler J VanderWeele; Ilya Shpitser
Journal:  Biometrics       Date:  2011-05-31       Impact factor: 2.571

5.  Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference.

Authors:  Mireille E Schnitzer; Judith J Lok; Susan Gruber
Journal:  Int J Biostat       Date:  2016-05-01       Impact factor: 0.968
  5 in total

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