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. However, most existing causal methods
cannot generalize to predicting the effects of previously unseen interventions
(e.g., a newly invented drug), because they require data for individuals who
received the intervention. Here, we consider zero-shot causal learning:
predicting the personalized effects of novel, previously unseen interventions.
To tackle this problem, we propose CaML, a causal meta-learning framework which
formulates the personalized prediction of each intervention's effect as a task.
Rather than training a separate model for each intervention, CaML trains as a
single meta-model across thousands of tasks, each constructed by sampling an
intervention and individuals who either did or did not receive it. 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 unseen interventions. Experimental results on real
world datasets in large-scale medical claims and cell-line perturbations
demonstrate the effectiveness of our approach. Most strikingly, CaML zero-shot
predictions outperform even strong baselines which have direct access to data
of considered target interventions.

本研究提出了一种无需先前数据就可以预测新型干预效果的因果元学习框架 (CaML)，它是一种单一元模型，可跨越数千个任务进行训练，结合基于个体特征和干预信息的元特征，证明了在医疗和细胞实验中的有效性。