  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.
