COCA mediation estimator: negative-control calibration of both stages

Uses the negative-control outcome W to calibrate both the mediator and outcome regressions, extending fit_coca to the mediation setting.

Usage

fit_coca_mediation(y, X, M, w, covars = NULL, ratio_cap = 10)

Arguments

y

Numeric primary outcome vector (length n).

X

Numeric exposure vector (length n).

M

Numeric mediator vector (length n).

w

Numeric negative-control outcome vector (length n). Recommended: pass rowMeans(W_matrix) for stability.

covars

Optional data frame of additional covariates (n rows).

ratio_cap

Maximum absolute value of any ratio estimate before flagging as unstable. Default 10.

Value

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

Details

Stage 1: W ~ M + X -> calibrated alpha_M = -beta_X / beta_M. Stage 2: W ~ Y + X + M -> calibrated NDE = -beta_X / beta_Y, calibrated beta_M = -beta_M / beta_Y. NIE = alpha_M * beta_M (both calibrated).

Examples

set.seed(1)
dat <- generate_toy_data(n = 200, mo_confounding = 0.8, seed = 1)
fit_coca_mediation(dat$Y[, 1], dat$X, dat$M, rowMeans(dat$W))
#> $NDE
#>         X 
#> -5.482667 
#> 
#> $NDE_se
#> [1] 3.891432
#> 
#> $NDE_p
#>         X 
#> 0.1588626 
#> 
#> $NIE
#>        X 
#> 5.734214 
#> 
#> $NIE_se
#> [1] 3.882897
#> 
#> $NIE_p
#>         X 
#> 0.1397326 
#> 
#> $alpha_M
#>         X 
#> 0.4962959 
#> 
#> $alpha_se
#> [1] 0.01958304
#> 
#> $beta_M
#>        M 
#> 11.55402 
#> 
#> $beta_M_se
#> [1] 7.81046
#>