Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty

TL;DR

This paper introduces a feature selection method that learns relevant causal structures from data to improve the accuracy of heterogeneous treatment effect estimation, especially when causal mechanisms are unknown.

Contribution

The paper proposes a novel feature selection approach that considers causal structure uncertainty, enhancing existing HTE estimation methods across various datasets.

Findings

Improved HTE estimation accuracy on synthetic and real-world data.

Lower estimation error achieved with the proposed feature selection method.

Effective handling of arbitrary causal structures in observational data.

Abstract

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.