Estimate sensitivity of ACDE estimates under varying levels of unobserved confounding

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)