  We present a domain-general account of causation that applies to settings in
which macro-level causal relations between two systems are of interest, but the
relevant causal features are poorly understood and have to be aggregated from
vast arrays of micro-measurements. Our approach generalizes that of Chalupka et
al. (2015) to the setting in which the macro-level effect is not specified. We
formalize the connection between micro- and macro-variables in such situations
and provide a coherent framework describing causal relations at multiple levels
of analysis. We present an algorithm that discovers macro-variable causes and
effects from micro-level measurements obtained from an experiment. We further
show how to design experiments to discover macro-variables from observational
micro-variable data. Finally, we show that under specific conditions, one can
identify multiple levels of causal structure. Throughout the article, we use a
simulated neuroscience multi-unit recording experiment to illustrate the ideas
and the algorithms.
