  Twin revolutions 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. However, existing analysis methods for
causal excursion effects require prespecified features of the observed
high-dimensional history to build a working model for a critical nuisance
parameter. Machine learning appears ideal for automatic feature construction,
but their naive application can lead to bias under model misspecification. To
address this issue, this paper revisits the estimation of causal excursion
effects from a meta-learner perspective, where the analyst remains agnostic to
the supervised learning algorithms used to estimate nuisance parameters. We
present the bidirectional asymptotic properties of the proposed estimators and
compare them both theoretically and through extensive simulations. The results
show relative efficiency gains and support the suggestion of a doubly robust
alternative to existing methods. Finally, the proposed methods' practical
utilities are demonstrated by analyzing data from a multi-institution cohort of
first-year medical residents in the United States (NeCamp et al., 2020).
