Diagnose adjustment-set and role robustness to uncertain edge directions

Given a DAG plus one or more edges whose direction is uncertain (a PDAG), enumerate every acyclic orientation and report whether the minimal/canonical adjustment sets or covariate roles change – i.e., whether your estimand is robust to that structural uncertainty.

Usage

pdag_robustness(
  dag,
  exposure,
  outcome,
  uncertain_edges = NULL,
  pdag = NULL,
  formula = NULL,
  max_uncertain = 10L
)

Arguments

dag

A dagitty DAG (the "root" model).

exposure, outcome

Optional; inferred from the DAG when omitted.

uncertain_edges

Character vector like c("A -- B") naming edges whose direction is unknown.

pdag

Optional dagitty PDAG; its -- edges are treated as uncertain.

formula

Optional model formula; used to decide whether an ambiguous covariate is actually in your specification (affects re-estimation advice).

max_uncertain

Integer guard on the number of uncertain edges (default 10 -> up to 1024 worlds).

Value

A DAGassist_pdag_summary object (printed as a bullet summary).

Examples

# What if we're unsure which way two arrows point?
pdag_robustness(toy_dag, uncertain_edges = c("Z -- X", "M -- Y"))
#> 
#> PDAG robustness summary:
#> - uncertain edges specified: 2
#> - worlds evaluated (acyclic orientations): 4
#> - minimal adjustment set changed: yes
#> - canonical adjustment set changed: yes
#> - covariate role changed: mediator -> ambiguous (collider / mediator) for M
#> - covariate role changed: confounder -> ambiguous (confounder / mediator) for Z (good/bad control flip)
#> - re-estimation recommended: yes