title: Signed directed acyclic graphs for causal inference.
abstract: Formal rules governing signed edges on causal directed acyclic graphs are described in this paper and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an intervention in the presence of intermediate variables. The incorporation of signed edges into the directed acyclic graph causal framework furthermore allows for the development of rules governing the relationship between monotonic effects and the sign of the covariance between two variables. It is shown that when certain assumptions about monotonic effects can be made then these results can be used to draw conclusions about the presence of causal effects even when data is missing on confounding variables.
authors.affiliation: University of Chicago, Chicago, USA.
authors.forename: Tyler J
authors.initials: TJ
authors.lastname: VanderWeele
authors.affiliation: Harvard School of Public Health, Boston, USA.
authors.forename: James M
authors.initials: JM
authors.lastname: Robins
keywords: Bias; Causal inference; Confounding; Directed acyclic graphs; Structural equations