Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916496710565888 |
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| author | Chen, Ying Xu, Ziwei Inoue, Kotaro Ichise, Ryutaro |
| author_facet | Chen, Ying Xu, Ziwei Inoue, Kotaro Ichise, Ryutaro |
| contents | Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confounding biases. In finance, IV construction typically relies on pre-designed synthetic IVs, with effectiveness measured by specific algorithms. This classic paradigm cannot be generalized to address broader issues that require more and specific IVs. Therefore, we propose an expertise-driven model (ETE-FinCa) to optimize the source of expertise, instantiate IVs by the expertise concept, and interpret the cause-effect relationship by integrating concept with real economic data. The results show that the feature selection based on causal knowledge graphs improves the classification performance than others, with up to a 11.7% increase in accuracy and a 23.0% increase in F1-score. Furthermore, the high-quality IVs we defined can identify causal relationships between the treatment and outcome variables in the Two-Stage Least Squares Regression model with statistical significance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_17542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation Chen, Ying Xu, Ziwei Inoue, Kotaro Ichise, Ryutaro General Economics Economics Instrumental Variable (IV) provides a source of treatment randomization that is conditionally independent of the outcomes, responding to the challenges of counterfactual and confounding biases. In finance, IV construction typically relies on pre-designed synthetic IVs, with effectiveness measured by specific algorithms. This classic paradigm cannot be generalized to address broader issues that require more and specific IVs. Therefore, we propose an expertise-driven model (ETE-FinCa) to optimize the source of expertise, instantiate IVs by the expertise concept, and interpret the cause-effect relationship by integrating concept with real economic data. The results show that the feature selection based on causal knowledge graphs improves the classification performance than others, with up to a 11.7% increase in accuracy and a 23.0% increase in F1-score. Furthermore, the high-quality IVs we defined can identify causal relationships between the treatment and outcome variables in the Two-Stage Least Squares Regression model with statistical significance. |
| title | Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2411.17542 |