论文标题
积极学习因果概率树
Active learning of causal probability trees
论文作者
论文摘要
在过去的二十年中，人们对将因果信息（通常使用因果图）与机器学习模型相结合的因果信息越来越兴趣。概率树提供了因果信息的简单而强大的替代表示。它们既可以计算干预和反事实，并且严格笼统，因为它们允许与上下文依赖性因果关系依赖性。在这里，我们提出了一种从介入和观察数据的组合中学习概率树的贝叶斯方法。该方法量化了从干预措施中量化预期信息的增益，并以最大的收益选择干预措施。我们证明了该方法对模拟和真实数据的效率。在有限的介入预算上学习概率树的有效方法将大大扩展其适用性。
The past two decades have seen a growing interest in combining causal information, commonly represented using causal graphs, with machine learning models. Probability trees provide a simple yet powerful alternative representation of causal information. They enable both computation of intervention and counterfactuals, and are strictly more general, since they allow context-dependent causal dependencies. Here we present a Bayesian method for learning probability trees from a combination of interventional and observational data. The method quantifies the expected information gain from an intervention, and selects the interventions with the largest gain. We demonstrate the efficiency of the method on simulated and real data. An effective method for learning probability trees on a limited interventional budget will greatly expand their applicability.
