mlsanityData to 7 Oct 2026

Assessing Time-Varying Causal Effect Moderation in Mobile Health

arXiv 1601.00237 ↗First posted 3 Jan 2016stat.MEcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jan 2016.

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Citing papers per year: 2020: 1; 2021: 3; 2022: 7; 2023: 5; 2024: 3; 2025: 4; 2026: 4.

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  1. 2022A Review of Off-Policy Evaluation in Reinforcement Learningcited by 92
  2. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  3. 2022Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation Guidelinescited by 22
  4. 2022Testing Stationarity and Change Point Detection in Reinforcement Learningcited by 12
  5. 2022Doubly robust nearest neighbors in factor modelscited by 9
  6. 2020Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learningcited by 8
  7. 2022Policy Evaluation for Temporal and/or Spatial Dependent Experimentscited by 8
  8. 2022Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventionscited by 6
  9. 2023Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resamplingcited by 6
  10. 2024Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experimentscited by 6
  11. 2026Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directionscited by 5
  12. 2022A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Marketscited by 4
15 more
  1. 2021Adaptive Sequential Design for a Single Time-Seriescited by 3
  2. 2024Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trialscited by 3
  3. 2025Dynamic Causal Structure Discovery and Causal Effect Estimationcited by 3
  4. 2023PWSHAP: A Path-Wise Explanation Model for Targeted Variablescited by 2
  5. 2023Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health studycited by 2
  6. 2025Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventionscited by 1
  7. 2025Power Constrained Nonstationary Bandits with Habituation and Recovery Dynamicscited by 1
  8. 2021Dynamic covariate balancing: estimating treatment effects over time with potential local projectionscited by 0
  9. 2023Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selectioncited by 0
  10. 2023A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderationcited by 0
  11. 2024Online Uniform Sampling: Randomized Learning-Augmented Approximation Algorithms with Application to Digital Healthcited by 0
  12. 2025Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inferencecited by 0
  13. 2026Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completioncited by 0
  14. 2026When AI Generates Covariates: Causal Typing and Estimand Drift in Sequential Experimentscited by 0
  15. 2026Always-On Experimentationcited by 0