Regret Minimization for Causal Inference on Large Treatment Space
Regret Minimization for Causal Inference on Large Treatment Space
复制标题
DOI：
--
复制
发表时间：
2020-06
期刊：
影响因子：
--
通讯作者：
Akira Tanimoto;Tomoya Sakai;Takashi Takenouchi;H. Kashima
中科院分区：
文献类型：
--
作者：
Akira Tanimoto;Tomoya Sakai;Takashi Takenouchi;H. Kashima
预测哪种行动（治疗）将导致更好的结果是决策支持系统的中心任务。为了在真实的情况下建立预测模型，由于缺乏随机对照试验（RCT）数据，从有偏倚的观察数据中学习是一个关键问题。为了处理这种有偏差的观察数据，最近在因果推理和反事实机器学习方面的努力集中在对二元动作空间上的潜在结果的去偏估计以及它们之间的差异，即个体治疗效果。当涉及到大的动作空间时（例如，为患者选择适当的药物组合），然而，潜在结果的回归准确性在实践中不再足以实现良好的决策性能。这是因为大行动空间上的平均准确度并不能保证不存在可能误导整个决策的单一潜在结果错误估计。我们提出的损失最大限度地减少了分类错误的行动是否是相对良好的个人目标之间的所有可行的行动，这进一步提高了决策性能，我们证明。我们还提出了一个网络架构和正则化，提取一个去偏表示不仅从个人的功能，但也从偏置的行动更好地推广在大的动作空间。在合成和半合成数据集上进行的广泛实验证明了我们的方法对于大型组合动作空间的优越性。
Predicting which action (treatment) will lead to a better outcome is a central task in decision support systems. To build a prediction model in real situations, learning from biased observational data is a critical issue due to the lack of randomized controlled trial (RCT) data. To handle such biased observational data, recent efforts in causal inference and counterfactual machine learning have focused on debiased estimation of the potential outcomes on a binary action space and the difference between them, namely, the individual treatment effect. When it comes to a large action space (e.g., selecting an appropriate combination of medicines for a patient), however, the regression accuracy of the potential outcomes is no longer sufficient in practical terms to achieve a good decision-making performance. This is because the mean accuracy on the large action space does not guarantee the nonexistence of a single potential outcome misestimation that might mislead the whole decision. Our proposed loss minimizes a classification error of whether or not the action is relatively good for the individual target among all feasible actions, which further improves the decision-making performance, as we prove. We also propose a network architecture and a regularizer that extracts a debiased representation not only from the individual feature but also from the biased action for better generalization in large action spaces. Extensive experiments on synthetic and semi-synthetic datasets demonstrate the superiority of our method for large combinatorial action spaces.