Multiply-Robust Causal Change Attribution
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914939372830720 |
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| author | Quintas-Martinez, Victor Bahadori, Mohammad Taha Santiago, Eduardo Mu, Jeff Janzing, Dominik Heckerman, David |
| author_facet | Quintas-Martinez, Victor Bahadori, Mohammad Taha Santiago, Eduardo Mu, Jeff Janzing, Dominik Heckerman, David |
| contents | Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy that, given a causal model, combines regression and re-weighting methods to quantify the contribution of each causal mechanism. Our proposed methodology is multiply robust, meaning that it still recovers the target parameter under partial misspecification. We prove that our estimator is consistent and asymptotically normal. Moreover, it can be incorporated into existing frameworks for causal attribution, such as Shapley values, which will inherit the consistency and large-sample distribution properties. Our method demonstrates excellent performance in Monte Carlo simulations, and we show its usefulness in an empirical application. Our method is implemented as part of the Python library DoWhy (arXiv:2011.04216, arXiv:2206.06821). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08839 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multiply-Robust Causal Change Attribution Quintas-Martinez, Victor Bahadori, Mohammad Taha Santiago, Eduardo Mu, Jeff Janzing, Dominik Heckerman, David Methodology Machine Learning Econometrics Comparing two samples of data, we observe a change in the distribution of an outcome variable. In the presence of multiple explanatory variables, how much of the change can be explained by each possible cause? We develop a new estimation strategy that, given a causal model, combines regression and re-weighting methods to quantify the contribution of each causal mechanism. Our proposed methodology is multiply robust, meaning that it still recovers the target parameter under partial misspecification. We prove that our estimator is consistent and asymptotically normal. Moreover, it can be incorporated into existing frameworks for causal attribution, such as Shapley values, which will inherit the consistency and large-sample distribution properties. Our method demonstrates excellent performance in Monte Carlo simulations, and we show its usefulness in an empirical application. Our method is implemented as part of the Python library DoWhy (arXiv:2011.04216, arXiv:2206.06821). |
| title | Multiply-Robust Causal Change Attribution |
| topic | Methodology Machine Learning Econometrics |
| url | https://arxiv.org/abs/2404.08839 |