Learning Causal AMP Chain Graphs

Jose M. Peña
; Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:33-44, 2017.

Abstract

Andersson-Madigan-Perlman chain graphs were originally introduced to represent independence models. They have recently been shown to be suitable for representing causal models with additive noise. In this paper, we present an algorithm for learning causal chain graphs. The algorithm builds on the ideas by \citet{Hoyeretal.2009}, i.e. it exploits the nonlinearities in the data to identify the direction of the causal relationships. We also report experimental results on real-world data.

Cite this Paper


BibTeX
@InProceedings{pmlr-v73-pena17b, title = {Learning Causal AMP Chain Graphs}, author = {Jose M. Peña}, booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks}, pages = {33--44}, year = {2017}, editor = {Antti Hyttinen and Joe Suzuki and Brandon Malone}, volume = {73}, series = {Proceedings of Machine Learning Research}, month = {20--22 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v73/pena17b/pena17b.pdf}, url = {http://proceedings.mlr.press/v73/pena17b.html}, abstract = {Andersson-Madigan-Perlman chain graphs were originally introduced to represent independence models. They have recently been shown to be suitable for representing causal models with additive noise. In this paper, we present an algorithm for learning causal chain graphs. The algorithm builds on the ideas by \citet{Hoyeretal.2009}, i.e. it exploits the nonlinearities in the data to identify the direction of the causal relationships. We also report experimental results on real-world data.} }
Endnote
%0 Conference Paper %T Learning Causal AMP Chain Graphs %A Jose M. Peña %B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks %C Proceedings of Machine Learning Research %D 2017 %E Antti Hyttinen %E Joe Suzuki %E Brandon Malone %F pmlr-v73-pena17b %I PMLR %J Proceedings of Machine Learning Research %P 33--44 %U http://proceedings.mlr.press %V 73 %W PMLR %X Andersson-Madigan-Perlman chain graphs were originally introduced to represent independence models. They have recently been shown to be suitable for representing causal models with additive noise. In this paper, we present an algorithm for learning causal chain graphs. The algorithm builds on the ideas by \citet{Hoyeretal.2009}, i.e. it exploits the nonlinearities in the data to identify the direction of the causal relationships. We also report experimental results on real-world data.
APA
Peña, J.M.. (2017). Learning Causal AMP Chain Graphs. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:33-44

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