flag bad controls (mediator/collider/desc of Y) among a candidate set

Usage

bad_controls_in(dag, controls, exposure, outcome)

Arguments

dag: A dagitty DAG object.
controls: Character vector of variable names.
exposure: Character; exposure node name (X).
outcome: Character; outcome node name (Y).

Value

A character vector (possibly empty) containing the elements of controls that are identified as "bad controls".

This is essentially the inverse of pick_minimal_controls(), as it returns bad controls, rather than the minimal/canonical set of good controls

Examples

d <- ggdag::dagify(
Y ~ X + M + Z,
M ~ X + Z,
C ~ X + Y,
exposure = "X",
outcome = "Y")
# M: mediator / Z: confounder / C: collider

# hypothetical candidate controls
controls <- c("Z", "M", "C")

# Flag controls that would bias the total effect of X on Y:
bad_controls_in(d, controls = c("Z","M","C"), exposure = "X", outcome = "Y")
#> [1] "M" "C"

# expected: c("M", "C")  # mediator & collider are "bad controls"; Z is OK