Test whether a set fulfills the adjustment criterion, that means, it removes all confounding bias when estimating a *total* effect. This is an extension of Pearl's Back-door criterion (Shpitser et al, 2010; van der Zander et al, 2014; Perkovic et al, 2015) which is complete in the sense that either a set fulfills this criterion, or it does not remove all confounding bias.
isAdjustmentSet(x, Z, exposure = NULL, outcome = NULL)the input graph, a DAG, MAG, PDAG, or PAG.
vector of variable names.
name(s) of the exposure variable(s). If not given (default), then the exposure variables are supposed to be defined in the graph itself.
name(s) of the outcome variable(s), also taken from the graph if not given.
If the input graph is a MAG or PAG, then it must not contain any undirected edges (=hidden selection variables).
E. Perkovic, J. Textor, M. Kalisch and M. H. Maathuis (2015), A Complete Generalized Adjustment Criterion. In Proceedings of UAI 2015.
I. Shpitser, T. VanderWeele and J. M. Robins (2010), On the validity of covariate adjustment for estimating causal effects. In Proceedings of UAI 2010.