Consistency of causal inference under the additive noise model

Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We analyze a family of methods for statistical causal inference from sample under the so- called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting.

Original languageEnglish (US)
Title of host publication31st International Conference on Machine Learning, ICML 2014
PublisherInternational Machine Learning Society (IMLS)
Pages1849-1857
Number of pages9
ISBN (Electronic)9781634393973
StatePublished - Jan 1 2014
Event31st International Conference on Machine Learning, ICML 2014 - Beijing, China
Duration: Jun 21 2014Jun 26 2014

Publication series

Name31st International Conference on Machine Learning, ICML 2014
Volume2

Other

Other31st International Conference on Machine Learning, ICML 2014
CountryChina
CityBeijing
Period6/21/146/26/14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Software

Fingerprint Dive into the research topics of 'Consistency of causal inference under the additive noise model'. Together they form a unique fingerprint.

Cite this