mcee_config_sl: Configure SuperLearner for MCEE nuisance parameters

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

Configure SuperLearner for MCEE nuisance parameters

Description

Creates a configuration to fit nuisance parameters using SuperLearner via SuperLearner::SuperLearner(). Automatically selects among multiple learning algorithms.

Usage

mcee_config_sl(target, formula, SL.library = NULL, clipping = NULL)

Arguments

target	Character. Nuisance parameter name ("p", "q", "eta", "mu", "nu").
formula	RHS-only formula (e.g., ~ X1 + X2 + dp).
SL.library	Optional character vector of learner names. If NULL, uses default library: c("SL.mean", "SL.glm", "SL.gam").
clipping	Optional numeric vector c(lo, hi) to clip predictions into [lo, hi] for numerical stability.

Value

A configuration list for use with mcee_general.

Examples

# SuperLearner with default library
cfg_q <- mcee_config_sl("q", ~ dp + M + X1)

# SuperLearner with custom library
cfg_eta <- mcee_config_sl("eta", ~ dp + X1,
    SL.library = c("SL.glm", "SL.rf", "SL.ranger")
)

MRTAnalysis documentation built on Sept. 9, 2025, 5:41 p.m.