[2602.10370] Causal Effect Estimation with Learned Instrument Representations

Statistics > Machine Learning arXiv:2602.10370 (stat) [Submitted on 10 Feb 2026 (v1), last revised 7 Apr 2026 (this version, v2)] Title:Causal Effect Estimation with Learned Instrument Representations Authors:Frances Dean, Jenna Fields, Radhika Bhalerao, Marie Charpignon, Ahmed Alaa View a PDF of the paper titled Causal Effect Estimation with Learned Instrument Representations, by Frances Dean and Jenna Fields and Radhika Bhalerao and Marie Charpignon and Ahmed Alaa View PDF HTML (experimental) Abstract: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...