  Existing methods of explainable AI and interpretable ML cannot explain change
in the values of an output variable for a statistical unit in terms of the
change in the input values and the change in the "mechanism" (the function
transforming input to output). We propose two methods based on counterfactuals
for explaining unit-level changes at various input granularities using the
concept of Shapley values from game theory. These methods satisfy two key
axioms desirable for any unit-level change attribution method. Through
simulations, we study the reliability and the scalability of the proposed
methods. We get sensible results from a case study on identifying the drivers
of the change in the earnings for individuals in the US.
