Author:
Schölkopf Bernhard,Hogg David W.,Wang Dun,Foreman-Mackey Daniel,Janzing Dominik,Simon-Gabriel Carl-Johann,Peters Jonas
Abstract
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.
Publisher
Proceedings of the National Academy of Sciences
Reference23 articles.
1. Schölkopf B (2012) On causal and anticausal learning. Proceedings of the 29th International Conference on Machine Learning (ICML), eds Langford J Pineau J (Omnipress, New York), pp 1255–1262.
2. Pearl J (2000) Causality (Cambridge Univ Press, New York).
3. Spirtes P Glymour C Scheines R (1993) Causation, Prediction, and Search (MIT Press, Cambridge, MA), 2nd Ed.
4. Autonomy;Aldrich;Oxf Econ Pap,1989
5. Hoover KD (2008) Causality in economics and econometrics. Economics and Philosophy, eds Durlauf SN Blume LE (Palgrave Macmillan, New York), 2nd Ed, Vol 6.
Cited by
43 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献