论文标题
因果关系学习的损失功能
A Loss-Function for Causal Machine-Learning
论文作者
论文摘要
因果机 - 学习是关于预测处理的网络效应（真升）。鉴于治疗组和对照组的数据，它类似于标准监督学习问题。不幸的是，由于数据中缺乏点的真实值，因此没有类似定义的损失函数。由于缺乏这种损失功能，现代机器学习的许多进展并不直接适用。 我们提出了一种在这种情况下定义损失函数的新方法，该方法等于标准回归问题中的均方越（MSE）。我们的损失函数普遍适用，因此提供了一个通用标准，可以评估任何预测真正升级的模型/策略的质量。我们证明，尽管具有新颖的定义，但仍然可以直接在此损失功能上执行梯度下降以找到最佳拟合。这导致了一种培训任何基于参数的模型的新方法，例如深神经网络，以解决因果机学习问题，而无需通过元学习策略。
Causal machine-learning is about predicting the net-effect (true-lift) of treatments. Given the data of a treatment group and a control group, it is similar to a standard supervised-learning problem. Unfortunately, there is no similarly well-defined loss function due to the lack of point-wise true values in the data. Many advances in modern machine-learning are not directly applicable due to the absence of such loss function. We propose a novel method to define a loss function in this context, which is equal to mean-square-error (MSE) in a standard regression problem. Our loss function is universally applicable, thus providing a general standard to evaluate the quality of any model/strategy that predicts the true-lift. We demonstrate that despite its novel definition, one can still perform gradient descent directly on this loss function to find the best fit. This leads to a new way to train any parameter-based model, such as deep neural networks, to solve causal machine-learning problems without going through the meta-learner strategy.
