IV2SLS mediation estimator: instrumented exposure in both stages

Uses the genetic instrument G to purge U1 from X, then estimates the mediator and outcome regressions with the cleaned exposure.

Usage

fit_iv2sls_mediation(y, X, M, g, w, covars = NULL, min_f = 10)

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).

covars

Optional data frame of additional covariates (n rows).

min_f

Minimum acceptable partial F-statistic for the excluded instrument. Default 10.

Value

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

Details

Strategy:

1. X ~ G + W -> X_hat (purge U1 from X).

2. M ~ X_hat -> alpha_M (clean effect of X on M).

3. Y ~ X_hat + M + W -> NDE = beta_X, beta_M (OLS).

The IV cleans X of U1 confounding, but M remains endogenous via U1 -> M. With a single instrument, natural effects are not fully identified (Rudolph et al., 2024); NDE and NIE are approximations whose bias from M-O confounding is the key finding the simulation demonstrates.

References

Rudolph, K. E., et al. (2024). Natural direct and indirect effects with an instrumental variable. Biometrics.

Examples

set.seed(1)
dat <- generate_toy_data(n = 300, mo_confounding = 0.8, seed = 1)
fit_iv2sls_mediation(dat$Y[, 1], dat$X, dat$M, dat$G[, 1], dat$W[, 1])
#> $NDE
#> [1] -0.1252387
#> 
#> $NDE_se
#> [1] 0.02585698
#> 
#> $NDE_p
#> [1] 2.059719e-06
#> 
#> $NIE
#> [1] 0.6381454
#> 
#> $NIE_se
#> [1] 0.03637006
#> 
#> $NIE_p
#> [1] 6.393409e-69
#> 
#> $alpha_M
#> [1] 0.7874435
#> 
#> $alpha_se
#> [1] 0.03647012
#> 
#> $beta_M
#> [1] 0.8104016
#> 
#> $beta_M_se
#> [1] 0.0269171
#>