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Primary Submission Category: Machine Learning and Causal Inference

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

Authors: Jieru Shi, Walter Dempsey,

Presenting Author: Jieru Shi*

Twin revolutions in wearable technologies and digital health interventions have significantly expanded the accessibility and uptake of mobile health (mHealth) interventions in multiple domains of health sciences. Sequentially randomized experiments called micro-randomized trials (MRTs) have grown in popularity as a means to empirically evaluate the effectiveness of these mHealth intervention components. MRTs have motivated a new class of causal estimands, termed “causal excursion effects”, which allows health scientists to assess how intervention effectiveness changes over time or is moderated by individual characteristics, context, or responses in the past. However, current data analysis methods require pre-specified features of the observed high-dimensional history to construct a working model of an important nuisance parameter. Machine learning (ML) algorithms are ideal for automatic feature construction, but their naive application to causal excursion estimation can lead to bias under model misspecification and therefore incorrect conclusions about the effectiveness of interventions. In this paper, the estimation of causal excursion effects is revisited from a meta-learner’s perspective, where ML and statistical methods such as supervised learning and regression have been explored. Asymptotic properties of the novel estimands are presented and a theoretical comparison accompanied by extensive simulation experiments demonstrates relative efficiency gains.