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…