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  Identifiability of Cause and Effect using Regularized Regression

Marx, A., & Vreeken, J. (2019). Identifiability of Cause and Effect using Regularized Regression. In KDD'19 (pp. 852-861). New York, NY: ACM. doi:10.1145/3292500.3330854.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0004-858C-8 Version Permalink: https://hdl.handle.net/21.11116/0000-0006-3FB5-8
Genre: Conference Paper

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 Creators:
Marx, Alexander1, Author           
Vreeken, Jilles2, Author           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 20192019
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Marx_KDD2019
DOI: 10.1145/3292500.3330854
 Degree: -

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Title: 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Place of Event: Anchorage, AK, USA
Start-/End Date: 2019-08-04 - 2019-08-08

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Title: KDD'19
  Subtitle : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  Other : Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  Abbreviation : KDD 2019
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: New York, NY : ACM
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 852 - 861 Identifier: ISBN: 978-1-4503-6201-6