  Cause-and-effect reasoning, the attribution of effects to causes, is one of
the most powerful and unique skills humans possess. Multiple surveys are
mapping out causal attributions as networks, but it is unclear how well these
efforts can be combined. Further, the total size of the collective causal
attribution network held by humans is currently unknown, making it challenging
to assess the progress of these surveys. Here we study three causal attribution
networks to determine how well they can be combined into a single network.
Combining these networks requires dealing with ambiguous nodes, as nodes
represent written descriptions of causes and effects and different descriptions
may exist for the same concept. We introduce NetFUSES, a method for combining
networks with ambiguous nodes. Crucially, treating the different causal
attributions networks as independent samples allows us to use their overlap to
estimate the total size of the collective causal attribution network. We find
that existing surveys capture 5.77% $\pm$ 0.781% of the $\approx$293 000 causes
and effects estimated to exist, and 0.198% $\pm$ 0.174% of the $\approx$10 200
000 attributed cause-effect relationships.
