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：随着对利用个体高维特征属性估计异质性处理效应的兴趣日益增长，在高维异质性处理效应估计中实现高性能颇具挑战性，因为在此设定下，部分特征会引发样本选择偏差，而另一些虽不引发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，现有方法采用逆概率加权学习分离的特征表示。然而，由于逆概率加权权重在数值上不稳定，这些方法在有限样本设定下存在估计偏差。为通过加权表示学习开发数值稳健的估计器，我们提出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，通过有效修正权重值，我们的方法优于现有方法（包括传统加权方案）。我们的代码可在 https://github.com/ychika/DPSW 获取。