Estimating how a treatment affects units individually, known as heterogeneous
treatment effect (HTE) estimation, is an essential part of decision-making and
policy implementation. The accumulation of large amounts of data in many
domains, such as healthcare and e-commerce, has led to increased interest in
developing data-driven algorithms for estimating heterogeneous effects from
observational and experimental data. However, these methods often make strong
assumptions about the observed features and ignore the underlying causal model
structure, which can lead to biased HTE estimation. At the same time,
accounting for the causal structure of real-world data is rarely trivial since
the causal mechanisms that gave rise to the data are typically unknown. To
address this problem, we develop a feature selection method that considers each
feature's value for HTE estimation and learns the relevant parts of the causal
structure from data. We provide strong empirical evidence that our method
improves existing data-driven HTE estimation methods under arbitrary underlying
causal structures. Our results on synthetic, semi-synthetic, and real-world
datasets show that our feature selection algorithm leads to lower HTE
estimation error.

该研究通过开发一种考虑每个特征值对异质性治疗效果估计的特征选择方法，并从数据中学习相关部分的因果结构，为处理真实世界数据的因果结构问题提供了解决方法。研究结果表明，该方法在任意潜在因果结构下均优于现有的数据驱动异质性治疗效果估计方法，并可降低异质性治疗效果估计误差。