Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects

Yue Liu, Zhuangyan Fang, Yangbo He, Zhi Geng
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:290-299, 2020.

Abstract

It is clear that some causal effects cannot be identified from observational data when the causal directed acyclic graph is absent. In such cases, IDA is a useful framework which estimates all possible causal effects by adjusting for all possible parental sets. In this paper, we combine the adjustment set selection procedure with the original IDA framework. Our goal is to find a common set that can be subtracted from all possible parental sets without influencing the back-door adjustment. To this end, we first introduce graphical conditions to decide whether a treatment’s neighbor or parent in a completed partially directed acyclic graph (CPDAG) can be subtracted and then provide a procedure to construct a subtractable set from those subtractable vertices. We next combine the procedure with the IDA framework and provide a fully local modification of IDA. Experimental results show that, with our modification, both the number of possible parental sets and the size of each possible parental set enumerated by the modified IDA decrease, making it possible to estimate all possible causal effects more efficiently.

Cite this Paper


BibTeX
@InProceedings{pmlr-v124-liu20a, title = {Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects}, author = {Liu, Yue and Fang, Zhuangyan and He, Yangbo and Geng, Zhi}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {290--299}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/liu20a/liu20a.pdf}, url = { http://proceedings.mlr.press/v124/liu20a.html }, abstract = {It is clear that some causal effects cannot be identified from observational data when the causal directed acyclic graph is absent. In such cases, IDA is a useful framework which estimates all possible causal effects by adjusting for all possible parental sets. In this paper, we combine the adjustment set selection procedure with the original IDA framework. Our goal is to find a common set that can be subtracted from all possible parental sets without influencing the back-door adjustment. To this end, we first introduce graphical conditions to decide whether a treatment’s neighbor or parent in a completed partially directed acyclic graph (CPDAG) can be subtracted and then provide a procedure to construct a subtractable set from those subtractable vertices. We next combine the procedure with the IDA framework and provide a fully local modification of IDA. Experimental results show that, with our modification, both the number of possible parental sets and the size of each possible parental set enumerated by the modified IDA decrease, making it possible to estimate all possible causal effects more efficiently.} }
Endnote
%0 Conference Paper %T Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects %A Yue Liu %A Zhuangyan Fang %A Yangbo He %A Zhi Geng %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-liu20a %I PMLR %P 290--299 %U http://proceedings.mlr.press/v124/liu20a.html %V 124 %X It is clear that some causal effects cannot be identified from observational data when the causal directed acyclic graph is absent. In such cases, IDA is a useful framework which estimates all possible causal effects by adjusting for all possible parental sets. In this paper, we combine the adjustment set selection procedure with the original IDA framework. Our goal is to find a common set that can be subtracted from all possible parental sets without influencing the back-door adjustment. To this end, we first introduce graphical conditions to decide whether a treatment’s neighbor or parent in a completed partially directed acyclic graph (CPDAG) can be subtracted and then provide a procedure to construct a subtractable set from those subtractable vertices. We next combine the procedure with the IDA framework and provide a fully local modification of IDA. Experimental results show that, with our modification, both the number of possible parental sets and the size of each possible parental set enumerated by the modified IDA decrease, making it possible to estimate all possible causal effects more efficiently.
APA
Liu, Y., Fang, Z., He, Y. & Geng, Z.. (2020). Collapsible IDA: Collapsing Parental Sets for Locally Estimating Possible Causal Effects. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:290-299 Available from http://proceedings.mlr.press/v124/liu20a.html .

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