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Schweisthal, J.* ; Frauen, D.* ; Schröder, M.* ; Hess, K.* ; Kilbertus, N. ; Feuerriegel, S.*

Learning Representations of Instruments for Partial Identification of Treatment Effects.

In: (42nd International Conference on Machine Learning, ICML 2025, 13-19 July 2025, Vancouver). 2025. 53703-53727 (Proceedings of Machine Learning Research ; 267)
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Reliable estimation of treatment effects from observational data is important in many disciplines such as medicine. However, estimation is challenging when unconfoundedness as a standard assumption in the causal inference literature is violated. In this work, we leverage arbitrary (po-tentially high-dimensional) instruments to estimate bounds on the conditional average treatment effect (CATE). Our contributions are three-fold: (1) We propose a novel approach for partial identification through a mapping of instruments to a discrete representation space so that we yield valid bounds on the CATE. This is crucial for reliable decision-making in real-world applications. (2) We derive a two-step procedure that learns tight bounds using a tailored neural partitioning of the latent instrument space. As a result, we avoid instability issues due to numerical approximations or adversarial training. Furthermore, our procedure aims to reduce the estimation variance in finite-sample settings to yield more reliable estimates. (3) We show theoretically that our procedure obtains valid bounds while reducing estimation variance. We further perform extensive experiments to demonstrate the effectiveness across various settings. Overall, our procedure offers a novel path for practitioners to make use of potentially high-dimensional instruments (e.g., as in Mendelian randomization).
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Publication type Article: Conference contribution
Conference Title 42nd International Conference on Machine Learning, ICML 2025
Conference Date 13-19 July 2025
Conference Location Vancouver
Quellenangaben Volume: 267, Issue: , Pages: 53703-53727 Article Number: , Supplement: ,
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)