Distinguishing cause from effect using observational data: methods and 
benchmarks

Mooij, J.M.; Peters, J.; Janzing, D.; Zscheischler, J.; Schölkopf, B.

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_2403589_5

DetailsSummary

Released

Journal Article

Distinguishing cause from effect using observational data: methods and benchmarks

MPS-Authors

/persons/resource/persons84135

Peters, J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons75626

Janzing, D.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84193

Schölkopf, B.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

Link
(Any fulltext)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Mooij, J., Peters, J., Janzing, D., Zscheischler, J., & Schölkopf, B. (2016). Distinguishing cause from effect using observational data: methods and benchmarks. The Journal of Machine Learning Research, 17(1), 1103-1204. Retrieved from http://dl.acm.org/citation.cfm?id=2946645.2946677.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002D-1980-D

Abstract

There is no abstract available