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.

翻译：过去二十年来,人们越来越关注将通常使用因果图表的因果信息与机器学习模型相结合。 概率树提供了简单而有力的因果信息的替代表示方式。 它们既能计算干预和反事实,又能严格地概括,因为它们允许因果依赖。 这里我们介绍了一种巴耶斯方法,从干预和观察数据的组合中学习概率树。 这种方法量化了从干预中获得的预期信息,并选择了收益最大的干预措施。 我们展示了模拟和实际数据方法的效率。 以有限的干预预算学习概率树的有效方法将极大地扩大其适用性。