Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health

Liangyu Zhu, Wenbin Lu, Rui Song
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11588-11598, 2020.

Abstract

In this article, we propose novel structural nested models to estimate causal effects of continuous treatments based on mobile health data. To find the treatment regime which optimizes the short-term outcomes for the patients, we define the weighted lag K advantage. The optimal treatment regime is then defined to be the one which maximizes this advantage. This method imposes minimal assumptions on the data generating process. Statistical inference can also be provided for the estimated parameters. Simulation studies and an application to the Ohio type 1 diabetes dataset show that our method could provide meaningful insights for dose suggestions with mobile health data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-zhu20c, title = {Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health}, author = {Zhu, Liangyu and Lu, Wenbin and Song, Rui}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {11588--11598}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/zhu20c/zhu20c.pdf}, url = {https://proceedings.mlr.press/v119/zhu20c.html}, abstract = {In this article, we propose novel structural nested models to estimate causal effects of continuous treatments based on mobile health data. To find the treatment regime which optimizes the short-term outcomes for the patients, we define the weighted lag K advantage. The optimal treatment regime is then defined to be the one which maximizes this advantage. This method imposes minimal assumptions on the data generating process. Statistical inference can also be provided for the estimated parameters. Simulation studies and an application to the Ohio type 1 diabetes dataset show that our method could provide meaningful insights for dose suggestions with mobile health data.} }
Endnote
%0 Conference Paper %T Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health %A Liangyu Zhu %A Wenbin Lu %A Rui Song %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-zhu20c %I PMLR %P 11588--11598 %U https://proceedings.mlr.press/v119/zhu20c.html %V 119 %X In this article, we propose novel structural nested models to estimate causal effects of continuous treatments based on mobile health data. To find the treatment regime which optimizes the short-term outcomes for the patients, we define the weighted lag K advantage. The optimal treatment regime is then defined to be the one which maximizes this advantage. This method imposes minimal assumptions on the data generating process. Statistical inference can also be provided for the estimated parameters. Simulation studies and an application to the Ohio type 1 diabetes dataset show that our method could provide meaningful insights for dose suggestions with mobile health data.
APA
Zhu, L., Lu, W. & Song, R.. (2020). Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:11588-11598 Available from https://proceedings.mlr.press/v119/zhu20c.html.

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