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. 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.
Abstract (translated)
预测不同干预会对特定个体产生何种影响在各种领域,如个性化医疗、公共政策和在线营销等方面非常重要。然而,大多数现有因果方法无法泛化到预测未曾有过干预(如新创药物)的效果,因为它们需要获得接受干预的个体的数据。在这里,我们考虑零次因果学习:预测 novel、未曾见过的干预的个性化影响。为了解决这一问题,我们提出了 CaML,一个因果元学习框架,可以将每个干预的效果的个性化预测作为任务。而不是为每个干预训练单独的模型,CaML将作为单个元模型训练数千个任务,每个任务由样本一个干预和一个未接受该干预的个体组成。通过利用干预信息(如药物属性)和个人特征(如患者病史),CaML能够预测未曾见过的干预的个性化影响。在大规模医疗声称和细胞系扰动的实际数据集上的实验结果证明了我们的方法的有效性。最令人瞩目的是,CaML的零次因果预测甚至胜过具有直接访问被视为目标干预数据的强大基线。
URL
https://arxiv.org/abs/2301.12292
