Conformal Prediction for Causal Effects of Continuous Treatments
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arXiv
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| Format: | Preprint |
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2024
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| author | Schröder, Maresa Frauen, Dennis Schweisthal, Jonas Heß, Konstantin Melnychuk, Valentyn Feuerriegel, Stefan |
| author_facet | Schröder, Maresa Frauen, Dennis Schweisthal, Jonas Heß, Konstantin Melnychuk, Valentyn Feuerriegel, Stefan |
| contents | Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample prediction intervals for potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_03094 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction for Causal Effects of Continuous Treatments Schröder, Maresa Frauen, Dennis Schweisthal, Jonas Heß, Konstantin Melnychuk, Valentyn Feuerriegel, Stefan Machine Learning Artificial Intelligence Methodology Uncertainty quantification of causal effects is crucial for safety-critical applications such as personalized medicine. A powerful approach for this is conformal prediction, which has several practical benefits due to model-agnostic finite-sample guarantees. Yet, existing methods for conformal prediction of causal effects are limited to binary/discrete treatments and make highly restrictive assumptions such as known propensity scores. In this work, we provide a novel conformal prediction method for potential outcomes of continuous treatments. We account for the additional uncertainty introduced through propensity estimation so that our conformal prediction intervals are valid even if the propensity score is unknown. Our contributions are three-fold: (1) We derive finite-sample prediction intervals for potential outcomes of continuous treatments. (2) We provide an algorithm for calculating the derived intervals. (3) We demonstrate the effectiveness of the conformal prediction intervals in experiments on synthetic and real-world datasets. To the best of our knowledge, we are the first to propose conformal prediction for continuous treatments when the propensity score is unknown and must be estimated from data. |
| title | Conformal Prediction for Causal Effects of Continuous Treatments |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2407.03094 |