  Causal inference quantifies cause-effect relationships by estimating
counterfactual parameters from data. This entails using \emph{identification
theory} to establish a link between counterfactual parameters of interest and
distributions from which data is available. A line of work characterized
non-parametric identification for a wide variety of causal parameters in terms
of the \emph{observed data distribution}. More recently, identification results
have been extended to settings where experimental data from interventional
distributions is also available. In this paper, we use Single World
Intervention Graphs and a nested factorization of models associated with mixed
graphs to give a very simple view of existing identification theory for
experimental data. We use this view to yield general identification algorithms
for settings where the input distributions consist of an arbitrary set of
observational and experimental distributions, including marginal and
conditional distributions. We show that for problems where inputs are
interventional marginal distributions of a certain type (ancestral marginals),
our algorithm is complete.
