Group Shapley Value and Counterfactual Simulations in a Structural Model

TL;DR

This paper introduces the group Shapley value, a novel method for interpreting counterfactual changes in structural economic models by decomposing parameter differences into interpretable, additive contributions.

Contribution

It develops the group Shapley value framework, including robust decomposition techniques for missing data, and demonstrates its application in economic models and literature review.

Findings

Provides a new interpretability tool for structural models

Offers robust methods for incomplete data scenarios

Demonstrates practical utility through case studies

Abstract

We propose a variant of the Shapley value, the group Shapley value, to interpret counterfactual simulations in structural economic models by quantifying the importance of different components. Our framework compares two sets of parameters, partitioned into multiple groups, and applying group Shapley value decomposition yields unique additive contributions to the changes between these sets. The relative contributions sum to one, enabling us to generate an importance table that is as easily interpretable as a regression table. The group Shapley value can be characterized as the solution to a constrained weighted least squares problem. Using this property, we develop robust decomposition methods to address scenarios where inputs for the group Shapley value are missing. We first apply our methodology to a simple Roy model and then illustrate its usefulness by revisiting two published papers.