mcee_config_sl_user

Creates a configuration to fit nuisance parameters using SuperLearner
with a user-specified library (required parameter).

Provides methods to analyze micro-randomized trials (MRTs) with binary treatment options. Supports four types of analyses: (1) proximal causal excursion effects, including weighted and centered least squares (WCLS) for continuous proximal outcomes by Boruvka et al. (2018) <doi:10.1080/01621459.2017.1305274> and the estimator for marginal excursion effect (EMEE) for binary proximal outcomes by Qian et al. (2021) <doi:10.1093/biomet/asaa070>; (2) distal causal excursion effects (DCEE) for continuous distal outcomes using a two-stage estimator by Qian (2025) <doi:10.1093/biomtc/ujaf134>; (3) mediated causal excursion effects (MCEE) for continuous distal outcomes, estimating natural direct and indirect excursion effects in the presence of time-varying mediators by Qian (2025) <doi:10.48550/arXiv.2506.20027>; and (4) standardized proximal effect size estimation for continuous proximal outcomes, generalizing the approach in Luers et al. (2019) <doi:10.1007/s11121-017-0862-5> to allow adjustment for baseline and time-varying covariates for improved efficiency.

Tianchen Qian

MRTAnalysis

Assessing Proximal, Distal, and Mediated Causal Excursion
Effects for Micro-Randomized Trials

Shaolin Xiang

Zhaoxi Cheng

Xinyi Song

John Dziak

Audrey Boruvka

mcee_config_sl_user function

<dl><dt>target</dt>
<dd>Character. Nuisance parameter name ("p", "q", "eta", "mu", "nu").</dd>
<dt>formula</dt>
<dd>RHS-only formula (e.g., <code>~ X1 + X2 + dp</code>).</dd>
<dt>SL.library</dt>
<dd>Character vector of learner names (required).</dd>
<dt>clipping</dt>
<dd>Optional numeric vector <code>c(lo, hi)</code> to clip predictions
into [lo, hi] for numerical stability.</dd></dl>

Arguments

Configure SuperLearner with user-specified library for MCEE nuisance parameters — mcee_config_sl_user

<dl>

<dt>target</dt>
<dd>Character. Nuisance parameter name ("p", "q", "eta", "mu", "nu").</dd>


<dt>formula</dt>
<dd>RHS-only formula (e.g., <code>~ X1 + X2 + dp</code>).</dd>


<dt>SL.library</dt>
<dd>Character vector of learner names (required).</dd>


<dt>clipping</dt>
<dd>Optional numeric vector <code>c(lo, hi)</code> to clip predictions
into [lo, hi] for numerical stability.</dd>

</dl>

mcee_config_sl_user: Configure SuperLearner with user-specified library for MCEE nuisance parameters

Description

Usage

Value

Arguments

Examples