mcee_general: Mediated Causal Excursion Effects (configurable nuisance models)

Description

Like mcee, but each nuisance function is configured explicitly via config_* objects (formula/method/family or known).

Usage

mcee_general(
  data,
  id,
  dp,
  outcome,
  treatment,
  mediator,
  availability = NULL,
  time_varying_effect_form,
  config_p,
  config_q,
  config_eta,
  config_mu,
  config_nu,
  weight_per_row = NULL,
  verbose = TRUE
)

Value

An "mcee_fit" object; see mcee.

Arguments

data: A data.frame in long format (one row per id-by-decision point).
id: Character. Column name for subject identifier.
dp: Character. Column name for decision point index (must increase strictly within subject).
outcome: Character. Column name for distal outcome (constant within subject).
treatment: Character. Column name for treatment (coded 0/1).
mediator: Character. Column name for mediator.
availability: Optional character. Column name for availability (0/1). If NULL, all rows are treated as available.
time_varying_effect_form: RHS-only formula for the basis \(f(t)\) (e.g., ~ 1, ~ dp, ~ poly(dp,2)).
config_p, config_q, config_eta, config_mu, config_nu: Lists created by mcee_config_maker() or helpers (e.g., mcee_config_glm(), mcee_config_known(), mcee_config_ranger(), etc.).
weight_per_row: Optional numeric vector of row weights (nonnegative, length nrow(data)). If NULL, uniform within-id weights are used.
verbose: Logical; print progress messages.

Details

Use this wrapper for observational studies (estimate p) or when you want different learners per nuisance. The same data requirements as mcee apply.

Examples

Run this code

set.seed(1)
n <- 10
T <- 4
id <- rep(1:n, each = T)
dp <- rep(1:T, times = n)
A <- rbinom(n * T, 1, 0.5)
M <- rbinom(n * T, 1, plogis(-0.2 + 0.3 * A + 0.1 * dp))
Y <- ave(0.5 * A + 0.6 * M + 0.1 * dp + rnorm(n * T), id)
dat <- data.frame(id, dp, A, M, Y)

cfg <- list(
    p   = mcee_config_known("p", 0.5),
    q   = mcee_config_glm("q", ~ dp + M),
    eta = mcee_config_glm("eta", ~dp),
    mu  = mcee_config_glm("mu", ~ dp + M),
    nu  = mcee_config_glm("nu", ~dp)
)
fit_gen <- mcee_general(dat, "id","dp","Y","A","M",
    time_varying_effect_form = ~ dp,
    config_p=cfg$p, config_q=cfg$q, config_eta=cfg$eta, config_mu=cfg$mu, config_nu=cfg$nu)