Why did the distribution change?

TL;DR

This paper introduces a formal causal model-based method to identify the root causes of distribution changes, accounting for independent mechanism variations, demonstrated through simulations and a real-world income gender gap case study.

Contribution

It presents a novel approach using graphical causal models to attribute distribution changes to specific causal mechanisms, including independent and partial changes.

Findings

Effective attribution of distribution change to causal mechanisms

Simulation results validate the method's performance

Application to income disparity reveals key drivers

Abstract

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.