# Assessing Time-Varying Causal Effect Moderation in Mobile Health

arXiv: https://arxiv.org/abs/1601.00237 · HTML: https://mlsanity.com/paper/1601.00237/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2016.

## Built on by (most cited first, 27 of 27)

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- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling](https://mlsanity.com/paper/2304.05365/) (2023)
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- [A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets](https://mlsanity.com/paper/2202.10574/) (2022)
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- [Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials](https://mlsanity.com/paper/2402.17003/) (2024)
- [Dynamic Causal Structure Discovery and Causal Effect Estimation](https://mlsanity.com/paper/2501.06534/) (2025)
- [Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study](https://mlsanity.com/paper/2311.14359/) (2023)
- [Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions](https://mlsanity.com/paper/2505.16311/) (2025)
- [Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamics](https://mlsanity.com/paper/2511.02944/) (2025)
- [Dynamic covariate balancing: estimating treatment effects over time with potential local projections](https://mlsanity.com/paper/2103.01280/) (2021)
- [Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection](https://mlsanity.com/paper/2301.12553/) (2023)
- [A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation](https://mlsanity.com/paper/2306.16297/) (2023)
- [Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Health](https://mlsanity.com/paper/2402.01995/) (2024)
- [Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inference](https://mlsanity.com/paper/2512.08828/) (2025)
- [Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion](https://mlsanity.com/paper/2605.30319/) (2026)
- [When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experiments](https://mlsanity.com/paper/2609.17772/) (2026)
- [Always-On Experimentation](https://mlsanity.com/paper/2609.38695/) (2026)

## Abstract (arXiv)

In mobile health interventions aimed at behavior change and maintenance, treatments are provided in real time to manage current or impending high risk situations or promote healthy behaviors in near real time. Currently there is great scientific interest in developing data analysis approaches to guide the development of mobile interventions. In particular data from mobile health studies might be used to examine effect moderators-i.e., individual characteristics, time-varying context or past treatment response that moderate the effect of current treatment on a subsequent response. This paper introduces a formal definition for moderated effects in terms of potential outcomes, a definition that is particularly suited to mobile interventions, where treatment occasions are numerous, individuals are not always available for treatment, and potential moderators might be influenced by past treatment. Methods for estimating moderated effects are developed and compared. The proposed approach is illustrated using BASICS-Mobile, a smartphone-based intervention designed to curb heavy drinking and smoking among college students.