Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness

Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:1523-1531, 2017.

Abstract

Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canonical examples of settings where a closed-form expression exists for the causal effect of one variable on another, given the presence of a third variable. Both rely on faithfulness to infer that the latter only influences the target effect via the cause variable. In reality, it is likely that this assumption only holds approximately and that there will be at least some form of weak interaction. This brings about the paradoxical situation that, in the large-sample limit, no predictions are made, as detecting the weak edge invalidates the setting. We introduce an alternative approach by replacing strict faithfulness with a prior that reflects the existence of many ’weak’ (irrelevant) and ’strong’ interactions. We obtain a posterior distribution over the target causal effect estimator which shows that, in many cases, we can still make good estimates. We demonstrate the approach in an application on a simple linear-Gaussian setting, using the MultiNest sampling algorithm, and compare it with established techniques to show our method is robust even when strict faithfulness is violated.

Cite this Paper

BibTeX
@InProceedings{pmlr-v54-bucur17a, title = {{Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness}}, author = {Bucur, Ioan Gabriel and Claassen, Tom and Heskes, Tom}, booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics}, pages = {1523--1531}, year = {2017}, editor = {Singh, Aarti and Zhu, Jerry}, volume = {54}, series = {Proceedings of Machine Learning Research}, month = {20--22 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v54/bucur17a/bucur17a.pdf}, url = {https://proceedings.mlr.press/v54/bucur17a.html}, abstract = {Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canonical examples of settings where a closed-form expression exists for the causal effect of one variable on another, given the presence of a third variable. Both rely on faithfulness to infer that the latter only influences the target effect via the cause variable. In reality, it is likely that this assumption only holds approximately and that there will be at least some form of weak interaction. This brings about the paradoxical situation that, in the large-sample limit, no predictions are made, as detecting the weak edge invalidates the setting. We introduce an alternative approach by replacing strict faithfulness with a prior that reflects the existence of many ’weak’ (irrelevant) and ’strong’ interactions. We obtain a posterior distribution over the target causal effect estimator which shows that, in many cases, we can still make good estimates. We demonstrate the approach in an application on a simple linear-Gaussian setting, using the MultiNest sampling algorithm, and compare it with established techniques to show our method is robust even when strict faithfulness is violated.} }
Endnote
%0 Conference Paper %T Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness %A Ioan Gabriel Bucur %A Tom Claassen %A Tom Heskes %B Proceedings of the 20th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2017 %E Aarti Singh %E Jerry Zhu %F pmlr-v54-bucur17a %I PMLR %P 1523--1531 %U https://proceedings.mlr.press/v54/bucur17a.html %V 54 %X Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canonical examples of settings where a closed-form expression exists for the causal effect of one variable on another, given the presence of a third variable. Both rely on faithfulness to infer that the latter only influences the target effect via the cause variable. In reality, it is likely that this assumption only holds approximately and that there will be at least some form of weak interaction. This brings about the paradoxical situation that, in the large-sample limit, no predictions are made, as detecting the weak edge invalidates the setting. We introduce an alternative approach by replacing strict faithfulness with a prior that reflects the existence of many ’weak’ (irrelevant) and ’strong’ interactions. We obtain a posterior distribution over the target causal effect estimator which shows that, in many cases, we can still make good estimates. We demonstrate the approach in an application on a simple linear-Gaussian setting, using the MultiNest sampling algorithm, and compare it with established techniques to show our method is robust even when strict faithfulness is violated.
APA
Bucur, I.G., Claassen, T. & Heskes, T.. (2017). Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 54:1523-1531 Available from https://proceedings.mlr.press/v54/bucur17a.html.

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