Towards causal effect estimation with learned instrument representations

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 could 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 it empirical conditions corresponding to the defining properties of valid instruments (i.e., relevance and exclusion restriction). ZNet is compatible with a wide range of downstream two-stage IV estimators of causal effects. Our experiments demonstrate that ZNet (i) can recover ground-truth instruments when they already exist in the ambient feature space and (ii) constructs candidate latent instruments in the embedding space when no explicit IVs are available. However, instrument representations have inherent challenges which warrant caution and further research. This work explores when ZNet might be used as a module for causal inference in general observational settings.