  We describe a formal approach based on graphical causal models to identify
the "root causes" of the change in the probability distribution of variables.
After factorizing the joint distribution into conditional distributions of each
variable, given its parents (the "causal mechanisms"), we attribute the change
to changes of these causal mechanisms. This attribution analysis accounts for
the fact that mechanisms often change independently and sometimes only some of
them change. Through simulations, we study the performance of our distribution
change attribution method. We then present a real-world case study identifying
the drivers of the difference in the income distribution between men and women.
