mcee_userfit_nuisance: Mediated Causal Excursion Effects with user-supplied nuisance...

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

Mediated Causal Excursion Effects with user-supplied nuisance predictions

Description

Skips Stage-1 model fitting and uses user-provided nuisance predictions.

Usage

mcee_userfit_nuisance(
  data,
  id,
  dp,
  outcome,
  treatment,
  mediator,
  availability = NULL,
  time_varying_effect_form,
  p1,
  q1,
  eta1,
  eta0,
  mu1,
  mu0,
  nu1,
  nu0,
  weight_per_row = NULL,
  verbose = TRUE
)

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)).
p1, q1, eta1, eta0, mu1, mu0, nu1, nu0	Numeric vectors (or column names) of per-row predictions aligned with data. See Details for definitions.
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

Nuisance definitions:

• p1: P(A_t=1\mid H_t) (known in MRTs). (Technically, this is P(A_t=I_t\mid H_t), but the user is allowed to input P(A_t=1\mid H_t) and the function will automatically correct it by setting p1 = 1 when I_t = 0.)

• q1: P(A_t=1\mid H_t, M_t). (Technically, this is P(A_t=I_t\mid H_t, M_t), but the user is allowed to input P(A_t=1\mid H_t, M_t) and the function will automatically correct it by setting q1 = 1 when I_t = 0.)

• eta1, eta0: E(Y\mid H_t, A_t=1) and E(Y\mid H_t, A_t=0).

• mu1, mu0: E(Y\mid H_t, A_t=1, M_t) and E(Y\mid H_t, A_t=0, M_t).

• nu1, nu0: cross-world regressions; see vignette and paper for definitions.

If availability is provided, rows with I=0 are coerced to p1=q1=1 (and hence p0=q0=1); a warning is emitted if overrides occur.

Value

An "mcee_fit" object; see mcee.

See Also

mcee, mcee_general

Examples

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)

fit_usr <- mcee_userfit_nuisance(dat, "id","dp","Y","A","M",
    time_varying_effect_form = ~ dp,
    p1 = rep(0.5, nrow(dat)),
    q1 = runif(nrow(dat),.3,.7),
    eta1 = rnorm(nrow(dat)), eta0 = rnorm(nrow(dat)),
    mu1 = rnorm(nrow(dat)),  mu0 = rnorm(nrow(dat)),
    nu1 = rnorm(nrow(dat)),  nu0 = rnorm(nrow(dat)))

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