# Causal Effect Estimation and Optimal Dose Suggestions in Mobile Health

arXiv: https://arxiv.org/abs/2007.09812 · HTML: https://mlsanity.com/paper/2007.09812/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [Estimating Optimal Infinite Horizon Dynamic Treatment Regimes via pT-Learning](https://mlsanity.com/paper/2110.10719/) (2021)
- [A Survey of Deep Causal Models and Their Industrial Applications](https://mlsanity.com/paper/2209.08860/) (2022)
- [Quasi-optimal Reinforcement Learning with Continuous Actions](https://mlsanity.com/paper/2301.08940/) (2023)
- [Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework](https://mlsanity.com/paper/2309.13278/) (2023)
- [Revisiting Bellman Errors for Offline Model Selection](https://mlsanity.com/paper/2302.00141/) (2023)
- [Stackelberg Batch Policy Learning](https://mlsanity.com/paper/2309.16188/) (2023)
- [Deep Spectral Q-learning with Application to Mobile Health](https://mlsanity.com/paper/2301.00927/) (2023)

## Abstract (arXiv)

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 that optimizes the expected short-term outcomes for patients, we define a weighted lag-K advantage as the value function. The optimal treatment regime is then defined to be the one that maximizes the value function. Our method imposes minimal assumptions on the data generating process. Statistical inference is 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.