Zero-shot causal learning
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916134445383680 |
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| author | Nilforoshan, Hamed Moor, Michael Roohani, Yusuf Chen, Yining Šurina, Anja Yasunaga, Michihiro Oblak, Sara Leskovec, Jure |
| author_facet | Nilforoshan, Hamed Moor, Michael Roohani, Yusuf Chen, Yining Šurina, Anja Yasunaga, Michihiro Oblak, Sara Leskovec, Jure |
| contents | Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. However, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address. Here, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug's attributes) and individual features~(e.g., a patient's history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, \method's zero-shot predictions outperform even strong baselines trained directly on data from the test interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_12292 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Zero-shot causal learning Nilforoshan, Hamed Moor, Michael Roohani, Yusuf Chen, Yining Šurina, Anja Yasunaga, Michihiro Oblak, Sara Leskovec, Jure Machine Learning Artificial Intelligence Computers and Society Human-Computer Interaction Predicting how different interventions will causally affect a specific individual is important in a variety of domains such as personalized medicine, public policy, and online marketing. There are a large number of methods to predict the effect of an existing intervention based on historical data from individuals who received it. However, in many settings it is important to predict the effects of novel interventions (e.g., a newly invented drug), which these methods do not address. Here, we consider zero-shot causal learning: predicting the personalized effects of a novel intervention. We propose CaML, a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task. CaML trains a single meta-model across thousands of tasks, each constructed by sampling an intervention, its recipients, and its nonrecipients. By leveraging both intervention information (e.g., a drug's attributes) and individual features~(e.g., a patient's history), CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training. Experimental results on real world datasets in large-scale medical claims and cell-line perturbations demonstrate the effectiveness of our approach. Most strikingly, \method's zero-shot predictions outperform even strong baselines trained directly on data from the test interventions. |
| title | Zero-shot causal learning |
| topic | Machine Learning Artificial Intelligence Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2301.12292 |