论文标题
使用深神经网络的因果推断
Causal inference using deep neural networks
论文作者
论文摘要
在许多科学领域，观察数据的因果推断是一个核心问题。在这里，我们提出了一个一般监督的深度学习框架，该框架通过将输入向量转换为每对输入的图像状表示，从而消除因果关系。给定培训数据集，我们首先构建了归一化的经验概率密度分布（NEPDF）矩阵。然后，我们在NEPDF上培训卷积神经网络（CNN），以进行因果关系预测。我们在几个不同的模拟和现实世界数据上测试了该方法，并将其与因果推断的先前方法进行了比较。如我们所示，该方法是一般的，可以有效地处理非常大的数据集并根据先前的方法进行改进。
Causal inference from observation data is a core problem in many scientific fields. Here we present a general supervised deep learning framework that infers causal interactions by transforming the input vectors to an image-like representation for every pair of inputs. Given a training dataset we first construct a normalized empirical probability density distribution (NEPDF) matrix. We then train a convolutional neural network (CNN) on NEPDFs for causality predictions. We tested the method on several different simulated and real world data and compared it to prior methods for causal inference. As we show, the method is general, can efficiently handle very large datasets and improves upon prior methods.
