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.

我们提出了一种简单、快速而又高效的匹配方法，使用随机超平面分割来估计因果效应，它在大维度的协变量下优于传统匹配技术，并与最先进的深度学习方法竞争，并避免了计算昂贵的深度神经网络的训练。

随机超平面剖分的因果效应估计

Causal Effect Estimation Using Random Hyperplane Tessellations

The propensity score is a common tool for estimating the causal effect of a
binary treatment in observational data. In this setting, matching,
subclassification, imputation, or inverse probability weighting on the
propensity score can reduce the initial covariate bias between the treatment
and control groups. With more than two treatment options, however, estimation
of causal effects requires additional assumptions and techniques, the
implementations of which have varied across disciplines. This paper reviews
current methods, and it identifies and contrasts the treatment effects that
each one estimates. Additionally, we propose possible matching techniques for
use with multiple, nominal categorical treatments, and use simulations to show
how such algorithms can yield improved covariate similarity between those in
the matched sets, relative the pre-matched cohort. To sum, this manuscript
provides a synopsis of how to notate and use causal methods for categorical
treatments.

本文介绍了使用倾向得分法估算二元治疗的因果效应，探讨了一些减少治疗和对照组之间初始协变量偏差的方法，并针对多种分类治疗方法提出了可能的匹配技术，利用模拟演示了如何提高匹配集合中协变量的相似度。