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Addressing Instrument-Outcome Confounding in Mendelian Randomization through Representation Learning

arXiv 2602.19782 ↗First posted 23 Feb 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 44 references that are in the map. First posted on arXiv 23 Feb 2026.

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References · 20

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  2. 2015Variational Inference with Normalizing Flowscited by 2,161
  3. 2018Spectral Normalization for Generative Adversarial Networkscited by 1,618
  4. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 929
  5. 2019Variational Autoencoders and Nonlinear ICA: A Unifying Frameworkcited by 494
  6. 2018Invertible Residual Networkscited by 364
  7. 2021Self-Supervised Learning with Data Augmentations Provably Isolates Content from Stylecited by 308
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  9. 2016Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICAcited by 270
  10. 2021Contrastive Learning Inverts the Data Generating Processcited by 220
  11. 2022Interventional Causal Representation Learningcited by 140
  12. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
8 more
  1. 2024Unifying Causal Representation Learning with the Invariance Principlecited by 30
  2. 2023Multi-Domain Causal Representation Learning via Weak Distributional Invariancescited by 23
  3. 2022spred: Solving L1 Penalty with SGDcited by 16
  4. 2023Conditional Instrumental Variable Regression with Representation Learning for Causal Inferencecited by 8
  5. 2024Disentangled Representation Learning for Causal Inference with Instrumentscited by 6
  6. 2017The GENIUS Approach to Robust Mendelian Randomization Inferencecited by 5
  7. 2024Identification and Inference with Invalid Instrumentscited by 2
  8. 2017Extending the MR-Egger method for multivariable Mendelian randomization to correct for both measured and unmeasured pleiotropycited by 1

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