因果关系单调性下的治疗应答者分类
Classifying Treatment Responders Under Causal Effect Monotonicity
摘要：
在个体水平的因果推断的背景下，我们研究了预测某人是否会根据他们的特征和过去的特征、治疗指标（例如，药物/无药物），和二元结果（例如，从疾病中恢复）。作为一个分类任务，这个问题是困难的，不知道相反的治疗指标下的例子结果。我们假设这种影响是单调的，就像广告对购买的影响或保释金对出庭的影响一样：要么它会发生而不管治疗，要么它不会发生而不管治疗，要么它只会发生而取决于接受治疗的程度。预测后者是否是潜在的情况是我们的重点。虽然以前的工作集中在有条件的平均处理效应估计，制定一个分类任务，而不是一个估计任务的问题，使我们能够开发新的工具更适合这个问题。通过利用单调性，我们开发了新的判别和生成算法的响应者分类问题。我们探索和讨论连接到损坏的数据和政策学习。我们提供了一个实证研究与合成和真实的数据集比较这些专门的算法标准的基准。
英文摘要：
In the context of individual-level causal inference, we study the problem of predicting whether someone will respond or not to a treatment based on their features and past examples of features, treatment indicator (e.g., drug/no drug), and a binary outcome (e.g., recovery from disease). As a classification task, the problem is made difficult by not knowing the example outcomes under the opposite treatment indicators. We assume the effect is monotonic, as in advertising's effect on a purchase or bail-setting's effect on reappearance in court: either it would have happened regardless of treatment, not happened regardless, or happened only depending on exposure to treatment. Predicting whether the latter is latently the case is our focus. While previous work focuses on conditional average treatment effect estimation, formulating the problem as a classification task rather than an estimation task allows us to develop new tools more suited to this problem. By leveraging monotonicity, we develop new discriminative and generative algorithms for the responder-classification problem. We explore and discuss connections to corrupted data and policy learning. We provide an empirical study with both synthetic and real datasets to compare these specialized algorithms to standard benchmarks.
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