Learning Representations of Instruments for Partial Identification of Treatment Effects

Jonas Schweisthal, Dennis Frauen, Maresa Schröder, Konstantin Hess, Niki Kilbertus, Stefan Feuerriegel

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: Yes

Keywords: causal inference, Mendelian randomization, partial identification, instrumental variables, treatment effect, representation learning

TL;DR: We develop a novel method for partial identification of treatment effects by tailored representation learning of complex instruments, e.g., for Mendelian randomization

Abstract: Reliable estimation of treatment effects from observational data is crucial in fields like medicine, yet challenging when the unconfoundedness assumption is violated. We leverage arbitrary (potentially 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 by mapping instruments into a discrete representation space that yields valid CATE bounds, essential for reliable decision-making. (2) We derive a two-step procedure that learns tight bounds via neural partitioning of the latent instrument space, thereby avoiding instability from numerical approximations or adversarial training and reducing finite-sample variance. (3) We provide theoretical guarantees for valid bounds with reduced variance and demonstrate effectiveness through extensive experiments. Overall, our method offers a new avenue for practitioners to exploit high-dimensional instruments (e.g., in Mendelian randomization).

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Jonas_Schweisthal1

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 58