论文标题
主动不变因果预测：通过稳定选择实验选择
Active Invariant Causal Prediction: Experiment Selection through Stability
论文作者
论文摘要
因果学习的基本困难是，一般不能仅根据观察数据来完全识别因果模型。介入的数据，即源自不同实验环境的数据，可提高可识别性。但是，改进取决于每个实验中进行的干预措施的目标和性质。由于在实际应用实验中往往是昂贵的，因此需要执行正确的干预措施，以便尽可能少。在这项工作中，我们提出了基于不变因果预测（ICP）的新的主动学习（即实验选择）框架（A-ICP）（Peters等，2016）。对于一般的结构因果模型，我们表征了干预措施对所谓稳定集的影响，这是（Pfister等，2019）引入的概念。我们利用这些结果为A-ICP提出了几种干预选择策略，该策略迅速揭示了因果图中响应变量的直接原因，同时维持ICP中固有的误差控制。从经验上讲，我们分析了人口和有限政权实验中提议的政策的绩效。
A fundamental difficulty of causal learning is that causal models can generally not be fully identified based on observational data only. Interventional data, that is, data originating from different experimental environments, improves identifiability. However, the improvement depends critically on the target and nature of the interventions carried out in each experiment. Since in real applications experiments tend to be costly, there is a need to perform the right interventions such that as few as possible are required. In this work we propose a new active learning (i.e. experiment selection) framework (A-ICP) based on Invariant Causal Prediction (ICP) (Peters et al., 2016). For general structural causal models, we characterize the effect of interventions on so-called stable sets, a notion introduced by (Pfister et al., 2019). We leverage these results to propose several intervention selection policies for A-ICP which quickly reveal the direct causes of a response variable in the causal graph while maintaining the error control inherent in ICP. Empirically, we analyze the performance of the proposed policies in both population and finite-regime experiments.
