Instrumental variable (IV) methods mitigate bias from unobserved confounding in observational causal inference but rely on the availability of a valid instrument, which can often be difficult or infeasible to identify in practice. In this paper, we propose a representation learning approach that constructs instrumental representations from observed covariates, which enable IV-based estimation even in the absence of an explicit instrument. Our model (ZNet) achieves this through an architecture that mirrors the structural causal model of IVs; it decomposes the ambient feature space into confounding and instrumental components, and is trained by enforcing empirical moment conditions corresponding to the defining properties of valid instruments (i.e., relevance, exclusion restriction, and instrumental unconfoundedness). Importantly, ZNet is compatible with a wide range of downstream two-stage IV estimators of causal effects. Our experiments demonstrate that ZNet can (i) recover ground-truth instruments when they already exist in the ambient feature space and (ii) construct latent instruments in the embedding space when no explicit IVs are available. Our work suggests when ZNet can be used as a module for causal inference in general observational settings.

翻译：工具变量（IV）方法可减轻观测因果推断中未观测混杂因素带来的偏差，但其有效性依赖于实际中难以识别或不可获取的有效工具变量。本文提出一种基于表征学习的方法，通过从观测协变量中构建工具变量表征，使即便在缺乏显式工具变量的情形下仍能进行IV估计。本模型（ZNet）通过仿照工具变量结构因果模型的架构实现这一目标：将原始特征空间分解为混杂成分与工具成分，并通过施加与有效工具变量定义属性（相关性、排他性约束及工具无混杂性）对应的经验矩条件进行训练。重要的是，ZNet与多种下游两阶段因果效应IV估计方法兼容。实验表明，ZNet既能（i）在原始特征空间中存在真实工具变量时恢复其表征，也能（ii）在缺乏显式工具变量时在嵌入空间中构建潜在工具变量。本研究表明ZNet可作为通用观测场景下因果推断的模块化工具。