UNADJ binary mediation estimator: naive Baron-Kenny with logistic / LPM

Stage 1 (OLS): M ~ X -> alpha_M. Stage 2 (logistic / LPM): binary outcome ~ X + M -> NDE (coef on X), beta_M (coef on M). NIE = alpha_M * beta_M. No confounding adjustment; bias reference.

Usage

fit_unadj_mediation_bin(
  y,
  X,
  M,
  covars = NULL,
  effect_scale = c("logor", "riskdiff")
)

Arguments

y

Numeric 0/1 outcome vector (length n).

X

Numeric exposure vector (length n).

M

Numeric mediator vector (length n).

covars

Optional data frame of covariates (n rows).

effect_scale

Character: "logor" or "riskdiff".

Value

Named list: NDE, NDE_se, NDE_p, NIE, NIE_se, NIE_p, alpha_M, alpha_se, beta_M, beta_M_se.

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, outcome_type = "binary",
mo_confounding = 0.8, seed = 1)
fit_unadj_mediation_bin(dat$y_bin, dat$X, dat$M)
#> $NDE
#> [1] -1.325125
#> 
#> $NDE_se
#> [1] 0.4800089
#> 
#> $NDE_p
#> [1] 0.005769064
#> 
#> $NIE
#> [1] 1.853043
#> 
#> $NIE_se
#> [1] 0.4723713
#> 
#> $NIE_p
#> [1] 8.750686e-05
#> 
#> $alpha_M
#> [1] 0.7652063
#> 
#> $alpha_se
#> [1] 0.01901884
#> 
#> $beta_M
#> [1] 2.421625
#> 
#> $beta_M_se
#> [1] 0.6143711
#>