Zero-shot causal learning
- URL: http://arxiv.org/abs/2301.12292v4
- Date: Fri, 23 Feb 2024 00:05:03 GMT
- Title: Zero-shot causal learning
- Authors: Hamed Nilforoshan, Michael Moor, Yusuf Roohani, Yining Chen, Anja
\v{S}urina, Michihiro Yasunaga, Sara Oblak, Jure Leskovec
- Abstract summary: CaML is a causal meta-learning framework which formulates the personalized prediction of each intervention's effect as a task.
We show that CaML is able to predict the personalized effects of novel interventions that do not exist at the time of training.
- Score: 64.9368337542558
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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