A New Causal Decomposition Paradigm towards Health Equity

Xinwei Sun, Xiangyu Zheng, Jim Weinstein
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:875-890, 2023.

Abstract

Causal decomposition has provided a powerful tool to analyze health disparity problems by assessing the proportion of disparity caused by each mediator (the variable that mediates the effect of the exposure on the health outcome). However, most of these methods lack policy implications, as they fail to account for all sources of disparities caused by the mediator. Besides, its identifiability needs to specify a set to be admissible to make the strong ignorability condition hold, which can be problematic as some variables in this set may induce new spurious features. To resolve these issues, under the framework of the structural causal model, we propose a new decomposition, dubbed as adjusted and unadjusted effects, which is able to include all types of disparity by adjusting each mediator’s distribution from the disadvantaged group to the advantaged ones. Besides, by learning the maximal ancestral graph and implementing causal discovery from heterogeneous data, we can identify the admissible set, followed by an efficient algorithm for estimation. The theoretical correctness and efficacy of our method are demonstrated using a synthetic dataset and a common spine disease dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-sun23a, title = {A New Causal Decomposition Paradigm towards Health Equity}, author = {Sun, Xinwei and Zheng, Xiangyu and Weinstein, Jim}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {875--890}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/sun23a/sun23a.pdf}, url = {https://proceedings.mlr.press/v206/sun23a.html}, abstract = {Causal decomposition has provided a powerful tool to analyze health disparity problems by assessing the proportion of disparity caused by each mediator (the variable that mediates the effect of the exposure on the health outcome). However, most of these methods lack policy implications, as they fail to account for all sources of disparities caused by the mediator. Besides, its identifiability needs to specify a set to be admissible to make the strong ignorability condition hold, which can be problematic as some variables in this set may induce new spurious features. To resolve these issues, under the framework of the structural causal model, we propose a new decomposition, dubbed as adjusted and unadjusted effects, which is able to include all types of disparity by adjusting each mediator’s distribution from the disadvantaged group to the advantaged ones. Besides, by learning the maximal ancestral graph and implementing causal discovery from heterogeneous data, we can identify the admissible set, followed by an efficient algorithm for estimation. The theoretical correctness and efficacy of our method are demonstrated using a synthetic dataset and a common spine disease dataset.} }
Endnote
%0 Conference Paper %T A New Causal Decomposition Paradigm towards Health Equity %A Xinwei Sun %A Xiangyu Zheng %A Jim Weinstein %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-sun23a %I PMLR %P 875--890 %U https://proceedings.mlr.press/v206/sun23a.html %V 206 %X Causal decomposition has provided a powerful tool to analyze health disparity problems by assessing the proportion of disparity caused by each mediator (the variable that mediates the effect of the exposure on the health outcome). However, most of these methods lack policy implications, as they fail to account for all sources of disparities caused by the mediator. Besides, its identifiability needs to specify a set to be admissible to make the strong ignorability condition hold, which can be problematic as some variables in this set may induce new spurious features. To resolve these issues, under the framework of the structural causal model, we propose a new decomposition, dubbed as adjusted and unadjusted effects, which is able to include all types of disparity by adjusting each mediator’s distribution from the disadvantaged group to the advantaged ones. Besides, by learning the maximal ancestral graph and implementing causal discovery from heterogeneous data, we can identify the admissible set, followed by an efficient algorithm for estimation. The theoretical correctness and efficacy of our method are demonstrated using a synthetic dataset and a common spine disease dataset.
APA
Sun, X., Zheng, X. & Weinstein, J.. (2023). A New Causal Decomposition Paradigm towards Health Equity. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:875-890 Available from https://proceedings.mlr.press/v206/sun23a.html.

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