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.
翻译:预测不同的干预措施会如何因果影响特定个人,在诸如个性化医学、公共政策和在线营销等多个领域,不同干预措施将如何因果影响特定个人十分重要;然而,大多数现有的因果方法无法概括地预测先前未见干预措施(例如新发明的毒品)的影响,因为它们需要接受干预措施的个人的数据。在这里,我们考虑零射因果学习:预测以前未见新干预措施的因果影响。为了解决这一问题,我们提议一个因果的元学习框架CAML,该框架将每项干预措施的效果个人化预测作为一项任务。CAML培训作为每个干预措施的单独模型,而不是为每个干预措施培训一个单独的模型,而作为单一的元模型,跨越数千项任务,每个模型都是通过对干预进行取样而构建的,以及接受干预的个人需要数据。通过利用干预信息(例如药物的属性)和个体特征(例如病人的历史),CAML能够预测看不见干预措施的个性效应。在大规模医疗索赔中真实的世界数据集的实验结果和细胞透视线中,每个模型都展示了我们连续得到或没有接受干预的方法的有效性。