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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2207.09768 |
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| _version_ | 1866913462436757504 |
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| author | Quinzan, Francesco Casolo, Cecilia Muandet, Krikamol Luo, Yucen Kilbertus, Niki |
| author_facet | Quinzan, Francesco Casolo, Cecilia Muandet, Krikamol Luo, Yucen Kilbertus, Niki |
| contents | Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world. We propose graphical criteria that yield a sufficient condition for a predictor to be counterfactually invariant in terms of a conditional independence in the observational distribution. In order to learn such predictors, we propose a model-agnostic framework, called Counterfactually Invariant Prediction (CIP), building on the Hilbert-Schmidt Conditional Independence Criterion (HSCIC), a kernel-based conditional dependence measure. Our experimental results demonstrate the effectiveness of CIP in enforcing counterfactual invariance across various simulated and real-world datasets including scalar and multi-variate settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_09768 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Learning Counterfactually Invariant Predictors Quinzan, Francesco Casolo, Cecilia Muandet, Krikamol Luo, Yucen Kilbertus, Niki Machine Learning Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world. We propose graphical criteria that yield a sufficient condition for a predictor to be counterfactually invariant in terms of a conditional independence in the observational distribution. In order to learn such predictors, we propose a model-agnostic framework, called Counterfactually Invariant Prediction (CIP), building on the Hilbert-Schmidt Conditional Independence Criterion (HSCIC), a kernel-based conditional dependence measure. Our experimental results demonstrate the effectiveness of CIP in enforcing counterfactual invariance across various simulated and real-world datasets including scalar and multi-variate settings. |
| title | Learning Counterfactually Invariant Predictors |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2207.09768 |