Group Shapley Value and Counterfactual Simulations in a Structural Model
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929533798580224 |
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| author | Kwon, Yongchan Lee, Sokbae Pouliot, Guillaume A. |
| author_facet | Kwon, Yongchan Lee, Sokbae Pouliot, Guillaume A. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_06875 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Group Shapley Value and Counterfactual Simulations in a Structural Model Kwon, Yongchan Lee, Sokbae Pouliot, Guillaume A. Econometrics Machine Learning Methodology 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. |
| title | Group Shapley Value and Counterfactual Simulations in a Structural Model |
| topic | Econometrics Machine Learning Methodology |
| url | https://arxiv.org/abs/2410.06875 |