Modeling confounding by half-sibling regression

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 independ...

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Bibliografiska uppgifter
I publikationen:Proc Natl Acad Sci U S A
Huvudupphovsmän: Schölkopf, Bernhard, Hogg, David W., Wang, Dun, Foreman-Mackey, Daniel, Janzing, Dominik, Simon-Gabriel, Carl-Johann, Peters, Jonas
Materialtyp: Artigo
Språk:Inglês
Publicerad: National Academy of Sciences 2016
Ämnen:
Sackler Colloquium on Drawing Causal Inference from Big Data
Länkar:https://ncbi.nlm.nih.gov/pmc/articles/PMC4941423/
https://ncbi.nlm.nih.gov/pubmed/27382154
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1073/pnas.1511656113
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