  Our aim is to detect mechanistic interaction between the effects of two
causal factors on a binary response, as an aid to identifying situations where
the effects are mediated by a common mechanism. We propose a formalization of
mechanistic interaction which acknowledges asymmetries of the kind "factor A
interferes with factor B, but not viceversa". A class of tests for mechanistic
interaction is proposed, which works on discrete or continuous causal
variables, in any combination. Conditions under which these tests can be
applied under a generic regime of data collection, be it interventional or
observational, are discussed in terms of conditional independence assumptions
within the framework of Augmented Directed Graphs. The scientific relevance of
the method and the practicality of the graphical framework are illustrated with
the aid of two studies in coronary artery disease. Our analysis relies on the
"deep determinism" assumption that there exists some relevant set V - possibly
unobserved - of "context variables", such that the response Y is a
deterministic function of the values of V and of the causal factors of
interest. Caveats regarding this assumption in real studies are discussed.
