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[2602.19782] Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning
Summary
This article presents a novel representation learning framework aimed at addressing instrument-outcome confounding in Mendelian Randomization (MR), enhancing the accuracy of causal effect estimates in epidemiological research.
Why It Matters
Mendelian Randomization is crucial for understanding causal relationships in epidemiology, but confounding factors can skew results. This framework offers a solution to improve the reliability of MR studies, which is significant for public health and genetic research.
Key Takeaways
- Introduces a representation learning framework to tackle confounding in MR.
- Demonstrates theoretical guarantees for identifying latent instruments.
- Utilizes multi-environment data to enhance the robustness of causal estimates.
- Validates the approach through simulations and semi-synthetic experiments.
- Addresses common violations of MR assumptions, improving research accuracy.
Computer Science > Machine Learning arXiv:2602.19782 (cs) [Submitted on 23 Feb 2026] Title:Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning Authors:Shimeng Huang, Matthew Robinson, Francesco Locatello View a PDF of the paper titled Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning, by Shimeng Huang and 2 other authors View PDF HTML (experimental) Abstract:Mendelian Randomization (MR) is a prominent observational epidemiological research method designed to address unobserved confounding when estimating causal effects. However, core assumptions -- particularly the independence between instruments and unobserved confounders -- are often violated due to population stratification or assortative mating. Leveraging the increasing availability of multi-environment data, we propose a representation learning framework that exploits cross-environment invariance to recover latent exogenous components of genetic instruments. We provide theoretical guarantees for identifying these latent instruments under various mixing mechanisms and demonstrate the effectiveness of our approach through simulations and semi-synthetic experiments using data from the All of Us Research Hub. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2602.19782 [cs.LG] (or arXiv:2602.19782v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2602.19782 Focus to learn more arXiv-issued DOI via DataCite (p...
