Detecting the causal direction using elementary statistical
Hei Chan – National Institute of Advanced Industrial Science and Technology
Manabu Kuroki – Osaka University
We attempt to find the causal direction between two variables, where one is
known to causally influence the other, but not vice versa, given a data set of N samples of
the two variables. Our method involves the application of elementary statistical knowledge.
We first identify groups of data sets according to the strength of statistical dependencies,
and thus obtaining the relationships between different data generating processes and how
many roots (that is, causes) in each group. We then examine the extent the data is
truncated for each variable, allowing us to consider which variable to be manipulated or to
be stochastically manipulated. By combining the two steps, we judge the causal direction
between two variables in each dataset.