aiqbench analysis
This research introduces ZNet, a representation learning model designed to construct instrumental variables from observed covariates, addressing a key limitation in observational causal inference where valid instruments are hard to identify. By decomposing feature spaces into confounding and instrumental components, the approach enables two-stage IV estimators to function even without explicit external instruments, though it acknowledges inherent challenges requiring further investigation. The work represents a significant methodological advance for machine learning applications focused on unbiased effect estimation in complex datasets.
Towards causal effect estimation with learned instrument representations
arXiv:2602.10370v3 Announce Type: replace-cross 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 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.
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 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.
Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2602.10370 [stat.ML]
(or arXiv:2602.10370v3 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2602.10370 Focus to learn more arXiv-issued DOI via DataCite
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