  Causal emergence is the theory that macroscales can reduce the noise in
causal relationships, leading to stronger causes at the macroscale. First
identified using the effective information and later the integrated information
in model systems, causal emergence has been analyzed in real data across the
sciences since. But is it simply a quirk of these original measures? To answer
this question we examined over a dozen popular measures of causation, all
independently developed and widely used, and spanning different fields from
philosophy to statistics to psychology to genetics. All showed cases of causal
emergence. This is because, we prove, measures of causation are based on a
small set of related "causal primitives." This consilience of
independently-developed measures of causation shows that macroscale causation
is a general fact about causal relationships, is scientifically detectable, and
is not a quirk of any particular measure of causation. This finding sets the
science of emergence on firmer ground, opening the door for the detection of
intrinsic scales of function in complex systems, as well as assisting with
scientific modeling and experimental interventions.
