The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in judging the relative merits of false models. Edit distance, for example, fails to distinguish between strong and weak probabilistic dependencies. KL divergence, on the other hand, rewards equally all statistically equivalent models, regardless of their different causal claims. We propose an augmented KL divergence, which we call Causal KL (CKL), which takes into account causal relationships which distinguish between observationally equivalent models. Results are presented for three variants of CKL, showing that Causal KL works well in practice.
翻译:评估人工数据的因果模型发现的两个最常用的标准是编辑-距离和Kullback-leiber差异,从真正的模型到学习模型,这两个标准都对真实模型给予最大奖赏。然而,我们争辩说,在判断假模型的相对优点时,这两个标准都不够有区别。例如,编辑距离未能区分强性和弱性概率依赖性。另一方面,KL差异同样奖励所有统计等同模型,而不论其不同的因果索赔。我们建议增加KL差异,我们称之为Causal KL(CKL),其中考虑到区分观察等同模型的因果关系。对CKL的三个变体提出了结果,表明Causal KL在实践中效果良好。