论文标题
概率树和单一干预的价值
Probability trees and the value of a single intervention
论文作者
论文摘要
统计因果关系中最根本的问题是确定有限数据的因果关系。将先前因果结构与贝叶斯更新相结合的概率树被认为是可能的解决方案。在这项工作中，我们从单个干预措施中量化了信息增益，并表明预期的信息增益在进行干预之前和干预措施中的预期增益都具有简单的表达方式。这导致一种主动学习方法，该方法简单地选择了最高预期增益的干预措施，我们通过几个示例说明了这一点。我们的工作证明了概率树和贝叶斯对参数的估计如何为快速因果诱导提供了一种简单而可行的方法。
The most fundamental problem in statistical causality is determining causal relationships from limited data. Probability trees, which combine prior causal structures with Bayesian updates, have been suggested as a possible solution. In this work, we quantify the information gain from a single intervention and show that both the anticipated information gain, prior to making an intervention, and the expected gain from an intervention have simple expressions. This results in an active-learning method that simply selects the intervention with the highest anticipated gain, which we illustrate through several examples. Our work demonstrates how probability trees, and Bayesian estimation of their parameters, offer a simple yet viable approach to fast causal induction.
