  Networks (graphs) in psychology are often restricted to settings without
interventions. Here we consider a framework borrowed from biology that involves
multiple interventions from different contexts (observations and experiments)
in a single analysis. The method is called perturbation graphs. In gene
regulatory networks, the induced change in one gene is measured on all other
genes in the analysis, thereby assessing possible causal relations. This is
repeated for each gene in the analysis. A perturbation graph leads to the
correct set of causes (not necessarily direct causes). Subsequent pruning of
paths in the graph (called transitive reduction) should reveal direct causes.
We show that transitive reduction will not in general lead to the correct
underlying graph. There is however a close relation with another method, called
invariant causal prediction. Invariant causal prediction can be considered as a
generalisation of the perturbation graph method where including additional
variables (and so conditioning on those variables) does reveal direct causes,
and thereby replacing transitive reduction. We explain the basic ideas of
perturbation graphs, transitive reduction and invariant causal prediction and
investigate their connections. We conclude that perturbation graphs provide a
promising new tool for experimental designs in psychology, and combined with
invariant prediction make it possible to reveal direct causes instead of causal
paths. As an illustration we apply the perturbation graphs and invariant causal
prediction to a data set about attitudes on meat consumption.
