Abstract
Matching is one of the simplest approaches for estimating causal effects from observational data. Matching techniques compare the observed outcomes across pairs of individuals with similar covariate values but different treatment statuses in order to estimate causal effects. However, traditional matching techniques are unreliable given high-dimensional covariates due to the infamous curse of dimensionality. To overcome this challenge, we propose a simple, fast, yet highly effective approach to matching using Random Hyperplane Tessellations (RHPT). First, we prove that the RHPT representation is an approximate balancing score -- thus maintaining the strong ignorability assumption -- and provide empirical evidence for this claim. Second, we report results of extensive experiments showing that matching using RHPT outperforms traditional matching techniques and is competitive with state-of-the-art deep learning methods for causal effect estimation. In addition, RHPT avoids the need for computationally expensive training of deep neural networks.
Abstract (translated)
匹配是一种从观测数据中估计因果效应的简单方法之一。匹配技术将具有相似协方差值但不同处理状态的个体对之间的观测结果进行比较，以估计因果效应。然而，传统的匹配技术在高度维度的协方差数据下是不可靠的，因为著名的维度诅咒。为了克服这一挑战，我们提出了一种简单、快速、但效果极高的匹配方法——随机超平面镶嵌（RHPT）。首先，我们证明RHPT表示是一个近似的平衡分数，从而保持强大的忽略假设——并提供了关于这一说法的实证证据。其次，我们报告了使用RHPT进行匹配的广泛实验结果，表明其优于传统匹配技术，与最先进的深度学习方法在因果效应估计方面具有竞争关系。此外，RHPT避免了深度神经网络训练的计算成本。
URL
https://arxiv.org/abs/2404.10907
