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.

翻译：估计观察数据对个人和平均治疗的影响是保健和电子商务等许多领域的一个重要问题。在本文件中,我们主张平衡多头神经网络结构的正规化。我们的工作受到代表性学习技术的推动,以减少治疗和未经治疗的分布之间的差别,这种差别可能是由于混乱因素而产生。我们进一步将模型正规化,鼓励它预测治疗组中与控制组控制结果相似的个人的控制结果。我们从经验上研究了管理者不同重量之间的偏差权衡,以及诱导和转导推论之间的偏差权衡。