用分组沙普利值量化经济模型中各因素对反事实结果的影响。
Group Shapley Value and Counterfactual Simulations in a Structural Model
- 提出分组沙普利值，分解多组参数对结果变化的贡献。
- 贡献比例总和为1，可生成类似回归表的可解释重要性表格。
- 适用于缺失数据场景，适合政策评估与因果分析研究者。
我们提出一种沙普利值的变体——分组沙普利值，用于在结构经济模型中解释反事实模拟，量化不同组成部分的重要性。该框架比较两组参数（划分为多个子组），通过分组沙普利值分解可得到各组对结果变化的唯一可加贡献。各组相对贡献之和为1，从而生成类似回归表的直观重要性表格。分组沙普利值可表征为带约束的加权最小二乘问题的解，据此我们发展了应对输入缺失情形的稳健分解方法。首先在简单罗伊模型上应用该方法，随后通过重新分析两篇已发表论文，展示其实际效用。
原文摘要 · Abstract (English)
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.
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