LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

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

Photo from wikipedia

In this discussion we consider why it is important to estimate causal effect parameters well even they are not identified, propose a partially identified approach for causal inference in the… Click to show full abstract

In this discussion we consider why it is important to estimate causal effect parameters well even they are not identified, propose a partially identified approach for causal inference in the presence of colliders, point out an under-appreciated advantage of double robustness, discuss the relative difficulty of independence testing versus regression, and finally commend H\"aggstr\"om for her exploration of causal inference with high-dimensional confounding, while making a call for further research in this same vein.

Keywords: discussion data; driven confounder; confounder selection; data driven; selection via; discussion

Journal Title: Biometrics
Year Published: 2018

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.