There is a growing interest in estimating heterogeneous treatment effects across individuals using their high-dimensional feature attributes. Achieving high performance in such high-dimensional heterogeneous treatment effect estimation is challenging because in this setup, it is usual that some features induce sample selection bias while others do not but are predictive of potential outcomes. To avoid losing such predictive feature information, existing methods learn separate feature representations using inverse probability weighting (IPW). However, due to their numerically unstable IPW weights, these methods suffer from estimation bias under a finite sample setup. To develop a numerically robust estimator by weighted representation learning, we propose a differentiable Pareto-smoothed weighting framework that replaces extreme weight values in an end-to-end fashion. Our experimental results show that by effectively correcting the weight values, our proposed method outperforms the existing ones, including traditional weighting schemes. Our code is available at https://github.com/ychika/DPSW.

翻译：利用高维特征属性估计个体间的异质性处理效应正受到越来越多的关注。在这种高维异质性处理效应估计中实现高性能具有挑战性，因为在此设置下，通常某些特征会引入样本选择偏差，而其他特征虽不引入偏差却对潜在结果具有预测性。为避免丢失此类预测性特征信息，现有方法通过逆概率加权学习分离的特征表示。然而，由于其数值不稳定的IPW权重，这些方法在有限样本设置下存在估计偏差。为通过加权表示学习构建数值稳健的估计器，我们提出了一种可微帕累托平滑加权框架，以端到端方式替换极端权重值。实验结果表明，通过有效校正权重值，我们提出的方法优于现有方法（包括传统加权方案）。代码发布于https://github.com/ychika/DPSW。