PGC mediation estimator: bridge-function-adjusted natural effects (scalar bridge)

The original ICONIC PGC mediation implementation, which summarises the negative-control panel as a scalar (rowMeans(W)) before bridging. Like fit_pgc_scalar, this version is algebraically equivalent to the IV2SLS mediation estimator when the instrument is valid. Use fit_pgc_mediation (matrix bridge) when the completeness condition is of interest.

Usage

fit_pgc_scalar_mediation(y, X, M, g, w, covars = NULL)

Arguments

y

Numeric outcome vector (length n).

X

Numeric exposure vector (length n).

M

Numeric mediator vector (length n).

g

Numeric instrument vector (length n).

w

Numeric negative-control vector (length n). Pass rowMeans(W_matrix) for stability.

covars

Optional data frame of additional covariates (n rows).

Value

Named list: NDE, NDE_se, NDE_p, NIE, NIE_se, NIE_p.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, mo_confounding = 0.8, seed = 1)
fit_pgc_scalar_mediation(dat$Y[, 1], dat$X, dat$M, dat$G[, 1], rowMeans(dat$W))
#> $NDE
#> [1] -0.3304709
#> 
#> $NDE_se
#> [1] 0.04147903
#> 
#> $NDE_p
#> [1] 1.300326e-13
#> 
#> $NIE
#> [1] 0.6425387
#> 
#> $NIE_se
#> [1] 0.04652626
#> 
#> $NIE_p
#> [1] 2.211053e-43
#> 
#> $alpha_M
#> [1] 0.5454283
#> 
#> $alpha_se
#> [1] 0.02684247
#> 
#> $beta_M
#> [1] 1.178044
#> 
#> $beta_M_se
#> [1] 0.06257225
#>