cdesens: Estimate sensitivity of ACDE estimates under varying levels...

In DirectEffects: Estimating Controlled Direct Effects for Explaining Causal Findings

Description

Estimate how the Average Controlled Direct Effect varies by various levels of unobserved confounding. For each value of unmeasured confounding, summarized as a correlation between residuals, cdesens computes the ACDE. Standard errors are computed by a simple bootstrap.

Usage

cdesens(
  seqg,
  var,
  rho = seq(-0.9, 0.9, by = 0.05),
  bootstrap = c("none", "standard"),
  boots_n = 1000,
  verbose = FALSE,
  ...
)

Arguments

seqg	Output from sequential_g. The function only supports specifications with one mediator variable.
var	A character indicating the name of the variable for which the estimated ACDE is being evaluated.
rho	A numerical vector of correlations between errors to test for. The original model assumes rho = 0
bootstrap	character of c("none", "standard"), indicating whether to include bootstrap standard errors. Default is "none".
boots_n	Number of bootstrap replicates, defaults to 100.
verbose	Whether to show progress and messages, defaults to FALSE
...	Other parameters to pass on to lm.fit() when refitting the model

Examples

data(civilwar)


# main formula: Y ~ A + X | Z | M
form_main <- onset ~ ethfrac + lmtnest + ncontig + Oil | warl +
  gdpenl + lpop + polity2l + relfrac | instab

# estimate CDE
direct <- sequential_g(form_main, data = civilwar)

# sensitivity
out_sens <- cdesens(direct, var = "ethfrac")

# plot sensitivity
plot(out_sens)

DirectEffects documentation built on May 13, 2021, 1:08 a.m.