Modeling confounding by half-sibling regression

Research output: Contribution to journalJournal articlepeer-review

  • Bernhard Schölkopf
  • David W Hogg
  • Dun Wang
  • Daniel Foreman-Mackey
  • Dominik Janzing
  • Carl-Johann Simon-Gabriel
  • Jonas Peters

We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as "half-sibling regression," is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as time series data, respectively, and illustrate the potential of the method in a challenging astronomy application.

Original languageEnglish
JournalProceedings of the National Academy of Sciences of the United States of America
Volume113
Issue number27
Pages (from-to)7391-7398
Number of pages8
ISSN0027-8424
DOIs
Publication statusPublished - 5 Jul 2016
Externally publishedYes

    Research areas

  • Journal Article

ID: 165942011