Intervals of Causal Effects for Learning Causal Graphical Models

Samuel Montero-Hernandez, Felipe Orihuela-Espina, Luis Enrique Sucar
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:296-307, 2018.

Abstract

Structure learning algorithms aim to retrieve the true causal structure from a set of observations. Most times only an equivalence class can be recovered and a unique model cannot be singled out. We hypothesized that casual directions could be inferred from the assessment of the strength of potential causal effects and such assessment can be computed by intervals comparison strategies. We introduce SLICE (Structural Learning with Intervals of Causal Effects), a new algorithm to decide on unresolved relations, which taps on the computation of causal effects and an acceptability index; a strategy for intervals comparison. For validation purposes, synthetic datasets were generated varying the graph size and density with samples drawn from Gaussian and non-Gaussian distributions. Comparison against LiNGAM is made to establish the performance of SLICE over $1440$ scenarios using the normalised structural Hamming distance (SHD). The retrieved structures with SLICE showed smaller SHD values in the Gaussian case, improving the structure of the retrieved causal model in terms of correctly found directions. The acceptability index is a good predictor of the true causal effects ($R^2=0.62$). The proposed strategy represents a new tool for discovering unravelled causal relations in the presence of observational data only.

Cite this Paper

BibTeX
@InProceedings{pmlr-v72-montero-hernandez18a, title = {Intervals of Causal Effects for Learning Causal Graphical Models}, author = {Montero-Hernandez, Samuel and Orihuela-Espina, Felipe and Sucar, Luis Enrique}, booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, pages = {296--307}, year = {2018}, editor = {Kratochvíl, Václav and Studený, Milan}, volume = {72}, series = {Proceedings of Machine Learning Research}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/montero-hernandez18a/montero-hernandez18a.pdf}, url = {https://proceedings.mlr.press/v72/montero-hernandez18a.html}, abstract = {Structure learning algorithms aim to retrieve the true causal structure from a set of observations. Most times only an equivalence class can be recovered and a unique model cannot be singled out. We hypothesized that casual directions could be inferred from the assessment of the strength of potential causal effects and such assessment can be computed by intervals comparison strategies. We introduce SLICE (Structural Learning with Intervals of Causal Effects), a new algorithm to decide on unresolved relations, which taps on the computation of causal effects and an acceptability index; a strategy for intervals comparison. For validation purposes, synthetic datasets were generated varying the graph size and density with samples drawn from Gaussian and non-Gaussian distributions. Comparison against LiNGAM is made to establish the performance of SLICE over $1440$ scenarios using the normalised structural Hamming distance (SHD). The retrieved structures with SLICE showed smaller SHD values in the Gaussian case, improving the structure of the retrieved causal model in terms of correctly found directions. The acceptability index is a good predictor of the true causal effects ($R^2=0.62$). The proposed strategy represents a new tool for discovering unravelled causal relations in the presence of observational data only.} }
Endnote
%0 Conference Paper %T Intervals of Causal Effects for Learning Causal Graphical Models %A Samuel Montero-Hernandez %A Felipe Orihuela-Espina %A Luis Enrique Sucar %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-montero-hernandez18a %I PMLR %P 296--307 %U https://proceedings.mlr.press/v72/montero-hernandez18a.html %V 72 %X Structure learning algorithms aim to retrieve the true causal structure from a set of observations. Most times only an equivalence class can be recovered and a unique model cannot be singled out. We hypothesized that casual directions could be inferred from the assessment of the strength of potential causal effects and such assessment can be computed by intervals comparison strategies. We introduce SLICE (Structural Learning with Intervals of Causal Effects), a new algorithm to decide on unresolved relations, which taps on the computation of causal effects and an acceptability index; a strategy for intervals comparison. For validation purposes, synthetic datasets were generated varying the graph size and density with samples drawn from Gaussian and non-Gaussian distributions. Comparison against LiNGAM is made to establish the performance of SLICE over $1440$ scenarios using the normalised structural Hamming distance (SHD). The retrieved structures with SLICE showed smaller SHD values in the Gaussian case, improving the structure of the retrieved causal model in terms of correctly found directions. The acceptability index is a good predictor of the true causal effects ($R^2=0.62$). The proposed strategy represents a new tool for discovering unravelled causal relations in the presence of observational data only.
APA
Montero-Hernandez, S., Orihuela-Espina, F. & Sucar, L.E.. (2018). Intervals of Causal Effects for Learning Causal Graphical Models. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 72:296-307 Available from https://proceedings.mlr.press/v72/montero-hernandez18a.html.

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