Assessing Proximal, Distal, and Mediated Causal Excursion Effects for Micro-Randomized Trials
Distal Causal Excursion Effect (DCEE) Estimation
Assert that input is a data frame
Build basis matrix f(t) from time-varying effect formula
Build per-row weights omega(i,t) for MCEE estimation
Validate binary column coding
Validate control formula excludes treatment and outcome
Check that decision points are strictly increasing within each subject
Check config formula for inclusion/exclusion of mediator
Check that rows for each subject appear in contiguous blocks
Check data frame columns for missing/infinite values
Check numeric vector for missing/infinite values
Check that outcome is constant within each subject (required for dista...
Validate time-varying effect formula structure
Generate compact one-line description of nuisance model object
Numerical core implementing MCEE estimation mathematics
Select default GLM family based on nuisance parameter type
Remove a variable from RHS-only formula
Fit a single nuisance component with flexible learner support
Print informative message if no availability column provided
Print formatted coefficient table for MCEE results
Check that required columns exist in data frame
Resolve randomization probability from column name or scalar
Validate clipping bounds for probability predictions
Validate that learning method is supported
Extract variables from nuisance configuration formula
Extract variable names from RHS-only formula
Estimates the causal excursion effect for binary outcome MRT
Estimates the causal excursion effect for binary outcome MRT
Configure GAM for MCEE nuisance parameters
Configure GLM for MCEE nuisance parameters
Configure known constant values for MCEE nuisance parameters
Configure linear model for MCEE nuisance parameters
Build a nuisance-configuration object for mcee_general()
Configure Ranger Random Forest for MCEE nuisance parameters
Configure Random Forest for MCEE nuisance parameters
Configure SuperLearner with user-specified library for MCEE nuisance p...
Configure SuperLearner for MCEE nuisance parameters
Mediated Causal Excursion Effects (configurable nuisance models)
Two-stage helper for mediated causal excursion effects (MCEE)
Fit all nuisance models for MCEE Stage 1
Stage-2 MCEE parameter estimation given nuisance predictions
Mediated Causal Excursion Effects with user-supplied nuisance predicti...
Mediated Causal Excursion Effects for MRTs (streamlined)
Print method for summary of MCEE fits
Summary for DCEE fits
Summarize Causal Excursion Effect Fits for MRT with Binary Outcomes
Summarize an mcee fit
Summarize Causal Excursion Effect Fits for MRT with Continuous Outcome...
Estimates the causal excursion effect for continuous outcome MRT
Provides methods to analyze micro-randomized trials (MRTs) with binary treatment options. Supports three 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:10.48550/arXiv.2502.13500>; and (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>.