Consistency of causal inference under the additive noise model

Samory Kpotufe; Eleni Sgouritsa; Dominik Janzing; Bernhard Schölkopf

Consistency of causal inference under the additive noise model

Samory Kpotufe, Eleni Sgouritsa, Dominik Janzing, Bernhard Schölkopf

Operations Research & Financial Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 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 language	English (US)
Title of host publication	31st International Conference on Machine Learning, ICML 2014
Publisher	International Machine Learning Society (IMLS)
Pages	1849-1857
Number of pages	9
ISBN (Electronic)	9781634393973
State	Published - 2014
Event	31st International Conference on Machine Learning, ICML 2014 - Beijing, China Duration: Jun 21 2014 → Jun 26 2014

Publication series

Name	31st International Conference on Machine Learning, ICML 2014
Volume	2

Other

Other	31st International Conference on Machine Learning, ICML 2014
Country/Territory	China
City	Beijing
Period	6/21/14 → 6/26/14

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Software

Cite this

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title = "Consistency of causal inference under the additive noise model",

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.",

author = "Samory Kpotufe and Eleni Sgouritsa and Dominik Janzing and Bernhard Sch{\"o}lkopf",

note = "Publisher Copyright: Copyright {\textcopyright} (2014) by the International Machine Learning Society (IMLS) All rights reserved.; 31st International Conference on Machine Learning, ICML 2014 ; Conference date: 21-06-2014 Through 26-06-2014",

year = "2014",

language = "English (US)",

series = "31st International Conference on Machine Learning, ICML 2014",

publisher = "International Machine Learning Society (IMLS)",

pages = "1849--1857",

booktitle = "31st International Conference on Machine Learning, ICML 2014",

}

Kpotufe, S, Sgouritsa, E, Janzing, D & Schölkopf, B 2014, Consistency of causal inference under the additive noise model. in 31st International Conference on Machine Learning, ICML 2014. 31st International Conference on Machine Learning, ICML 2014, vol. 2, International Machine Learning Society (IMLS), pp. 1849-1857, 31st International Conference on Machine Learning, ICML 2014, Beijing, China, 6/21/14.

Consistency of causal inference under the additive noise model. / Kpotufe, Samory; Sgouritsa, Eleni; Janzing, Dominik et al.
31st International Conference on Machine Learning, ICML 2014. International Machine Learning Society (IMLS), 2014. p. 1849-1857 (31st International Conference on Machine Learning, ICML 2014; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Janzing, Dominik

AU - Schölkopf, Bernhard

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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M3 - Conference contribution

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T3 - 31st International Conference on Machine Learning, ICML 2014

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BT - 31st International Conference on Machine Learning, ICML 2014

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Y2 - 21 June 2014 through 26 June 2014

ER -

Consistency of causal inference under the additive noise model

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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