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Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health

Liangyu Zhu · Wenbin Lu · Rui Song

Keywords: [ Healthcare ] [ Kernel Methods ] [ Time Series and Sequence Models ] [ Causality ] [ Applications - Neuroscience, Cognitive Science, Biology and Health ]


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.

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