Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs

Jose M. Peña
; Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:21-32, 2017.

Abstract

Alternative acyclic directed mixed graphs (ADMGs) are graphs that may allow causal effect identification in scenarios where Pearl's original ADMGs may not, and vice versa. Therefore, they complement each other. In this paper, we introduce a sound algorithm for identifying arbitrary causal effects from alternative ADMGs. Moreover, we show that the algorithm is complete for identifying the causal effect of a single random variable on the rest. We also show that the algorithm follows from a calculus similar to Pearl's do-calculus.

Cite this Paper


BibTeX
@InProceedings{pmlr-v73-pena17a, title = {Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs}, author = {Jose M. Peña}, booktitle = {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks}, pages = {21--32}, 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/pena17a/pena17a.pdf}, url = {http://proceedings.mlr.press/v73/pena17a.html}, abstract = {Alternative acyclic directed mixed graphs (ADMGs) are graphs that may allow causal effect identification in scenarios where Pearl's original ADMGs may not, and vice versa. Therefore, they complement each other. In this paper, we introduce a sound algorithm for identifying arbitrary causal effects from alternative ADMGs. Moreover, we show that the algorithm is complete for identifying the causal effect of a single random variable on the rest. We also show that the algorithm follows from a calculus similar to Pearl's do-calculus. } }
Endnote
%0 Conference Paper %T Causal Effect Identification in Alternative Acyclic Directed Mixed 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-pena17a %I PMLR %J Proceedings of Machine Learning Research %P 21--32 %U http://proceedings.mlr.press %V 73 %W PMLR %X Alternative acyclic directed mixed graphs (ADMGs) are graphs that may allow causal effect identification in scenarios where Pearl's original ADMGs may not, and vice versa. Therefore, they complement each other. In this paper, we introduce a sound algorithm for identifying arbitrary causal effects from alternative ADMGs. Moreover, we show that the algorithm is complete for identifying the causal effect of a single random variable on the rest. We also show that the algorithm follows from a calculus similar to Pearl's do-calculus.
APA
Peña, J.M.. (2017). Causal Effect Identification in Alternative Acyclic Directed Mixed Graphs. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:21-32

Related Material