# A Meta-Learning Method for Estimation of Causal Excursion Effects to Assess Time-Varying Moderation

arXiv: https://arxiv.org/abs/2306.16297 · HTML: https://mlsanity.com/paper/2306.16297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation](https://mlsanity.com/paper/1810.12429/) (2018)
- [Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions](https://mlsanity.com/paper/1604.07125/) (2016)
- [Assessing Time-Varying Causal Effect Moderation in Mobile Health](https://mlsanity.com/paper/1601.00237/) (2016)
- [Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation](https://mlsanity.com/paper/2002.07285/) (2020)
- [Time series experiments and causal estimands: exact randomization tests and trading](https://mlsanity.com/paper/1706.07840/) (2017)
- [Debiased Machine Learning without Sample-Splitting for Stable Estimators](https://mlsanity.com/paper/2206.01825/) (2022)
- [Evaluating (weighted) dynamic treatment effects by double machine learning](https://mlsanity.com/paper/2012.00370/) (2020)
- [The Micro-Randomized Trial for Developing Digital Interventions: Experimental Design and Data Analysis Considerations](https://mlsanity.com/paper/2107.03544/) (2021)
- [A Simple and General Debiased Machine Learning Theorem with Finite Sample Guarantees](https://mlsanity.com/paper/2105.15197/) (2021)
- [Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes](https://mlsanity.com/paper/1906.00528/) (2019)
- [Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions](https://mlsanity.com/paper/2203.02605/) (2022)
- [Dynamic treatment effects: high-dimensional inference under model misspecification](https://mlsanity.com/paper/2111.06818/) (2021)
- [A causal inference framework for spatial confounding](https://mlsanity.com/paper/2112.14946/) (2021)

## Abstract (arXiv)

Advances in wearable technologies and health interventions delivered by smartphones have greatly increased the accessibility of mobile health (mHealth) interventions. Micro-randomized trials (MRTs) are designed to assess the effectiveness of the mHealth intervention and introduce a novel class of causal estimands called "causal excursion effects." These estimands enable the evaluation of how intervention effects change over time and are influenced by individual characteristics or context. Existing methods for analyzing causal excursion effects assume known randomization probabilities, complete observations, and a linear nuisance function with prespecified features of the high dimensional observed history. However, in complex mobile systems, these assumptions often fall short: randomization probabilities can be uncertain, observations may be incomplete, and the granularity of mHealth data makes linear modeling difficult. To address this issue, we propose a flexible and doubly robust inferential procedure, called "DR-WCLS," for estimating causal excursion effects from a meta-learner perspective. We present the bidirectional asymptotic properties of the proposed estimators and compare them with existing methods both theoretically and through extensive simulations. The results show a consistent and more efficient estimate, even with missing observations or uncertain treatment randomization probabilities. Finally, the practical utility of the proposed methods is demonstrated by analyzing data from a multiinstitution cohort of first-year medical residents in the United States (NeCamp et al., 2020).