mediate_tsls: Two-stage Least Squares Estimation of the Average Causal Mediation Effects

Description

Estimate quantities for causal mediation analysis using an instrumental variable estimator.

Usage

mediate_tsls(
  model.m,
  model.y,
  treat = "treat.name",
  conf.level = 0.95,
  robustSE = FALSE,
  cluster = NULL,
  boot = FALSE,
  sims = 1000,
  est_se = TRUE,
  ...
)

Value

mediate returns an object of class "mediate", "mediate.tsls", a list that contains the components listed below.

The function summary can be used to obtain a table of the results.

d1: point estimate for average causal mediation effects.
d1.ci: confidence intervals for average causal mediation effect. The confidence level is set at the value specified in 'conf.level'.
z0: point estimates for average direct effect.
z0.ci: confidence intervals for average direct effect.
z0.p: two-sided p-values for average causal direct effect.
n0: the "proportions mediated", or the size of the average causal mediation effect relative to the total effect.
n0.ci: confidence intervals for the proportion mediated.
n0.p: two-sided p-values for proportion mediated.
tau.coef: point estimate for total effect.
tau.ci: confidence interval for total effect.
tau.p: two-sided p-values for total effect.
boot: logical, the boot argument used.
treat: a character string indicating the name of the 'treat' variable used.
mediator: a character string indicating the name of the 'mediator' variable used.
INT: a logical value indicating whether the model specification allows the effects to differ between the treatment and control conditions.
conf.level: the confidence level used.
model.y: the outcome model used.
model.m: the mediator model used.
nobs: number of observations in the model frame for 'model.m' and 'model.y'. May differ from the numbers in the original models input to 'mediate' if 'dropobs' was 'TRUE'.
cluster: the clusters used.

Arguments

model.m: a fitted model object for mediator, of class lm.
model.y: a fitted model object for outcome, of class lm.
treat: a character string indicating the name of the treatment variable used in the models.
conf.level: level of the returned two-sided confidence intervals. Default is to return the 2.5 and 97.5 percentiles of the simulated quantities.
robustSE: a logical value. If 'TRUE', heteroskedasticity-consistent standard errors will be used. Default is 'FALSE'.
cluster: a variable indicating clusters for standard errors. Note that this should be a vector of cluster indicators itself, not a character string for the name of the variable.
boot: a logical value. if FALSE analytic confidence intervals based on Aroian (1947) will be used; if TRUE nonparametric bootstrap will be used. Default is FALSE.
sims: number of Monte Carlo draws for nonparametric bootstrap.
est_se: estimate standard errors. Primarily for internal use. Default is TRUE.
...: other arguments passed to vcovCL in the sandwich package: typically the type argument for heteroskedasticity-consistent standard errors.

References

Aroian, L. A. 1947. The probability function of the product of two normally distributed variables. *Annals of Mathematical Statistics,* 18, 265-271.

Examples

Run this code

# Generate data. We use TSLS to address unobserved confounding (n).
set.seed(123)
sims <- 1000
dat <- data.frame(z = sample(0:1, sims, replace = TRUE), 
                  t = sample(0:1, sims, replace = TRUE))
dat$n <- rnorm(sims, mean = 1)
dat$m <- rnorm(sims, mean = dat$z * 0.3 + dat$t * 0.2 + dat$n * 0.7, sd = 0.2)
dat$y <- rnorm(sims, mean = 5 + dat$t + dat$m * (-3) + dat$n, sd = 1)
model.m <- lm(m ~ t + z, data = dat)
model.y <- lm(y ~ t + m, data = dat)
cluster <- factor(sample(1:3, sims, replace = TRUE))
med <- mediate_tsls(model.m, model.y, cluster = cluster, treat = "t")
summary(med)

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