论文标题
平衡正规神经网络模型，用于因果效应估计
Balance Regularized Neural Network Models for Causal Effect Estimation
论文作者
论文摘要
在医疗保健和电子商务等许多领域中，从观察数据中估算个人和平均治疗效果是一个重要的问题。在本文中，我们主张平衡多头神经网络体系结构的平衡正规化。我们的工作是由代表性学习技术激励的，以减少因混杂因素而可能引起的未经处理和未经处理的分布之间的差异。我们通过鼓励模型预测治疗组中与对照组的控制结果相似的个人的控制结果进一步正规化。我们从经验上研究了正规化器不同权重之间的偏置变化权衡，以及电感和托管推理之间的偏差差异。
Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce. In this paper, we advocate balance regularization of multi-head neural network architectures. Our work is motivated by representation learning techniques to reduce differences between treated and untreated distributions that potentially arise due to confounding factors. We further regularize the model by encouraging it to predict control outcomes for individuals in the treatment group that are similar to control outcomes in the control group. We empirically study the bias-variance trade-off between different weightings of the regularizers, as well as between inductive and transductive inference.
