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.
name(s) of the exposure variable(s). If not given (default), then the
exposure variables are supposed to be defined in the graph itself.
outcome
name(s) of the outcome variable(s), also taken from the graph if
not given.
Details
If the input graph is a MAG or PAG, then it must not contain any undirected
edges (=hidden selection variables).
References
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.