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[2409.16407] Towards Representation Learning for Weighting Problems in Design-Based Causal Inference
Summary
This article explores the use of representation learning to improve weighting methods in design-based causal inference, addressing challenges in estimating causal effects without outcome information.
Why It Matters
Understanding representation learning in causal inference is crucial as it enhances the accuracy of estimating causal effects in various applications, such as survey weighting and cohort studies. This research proposes a novel framework that can lead to more reliable results in statistical analysis, which is vital for researchers and practitioners in the field.
Key Takeaways
- Representation learning can significantly improve weighting methods in causal inference.
- The proposed framework minimizes errors associated with representation choices.
- An end-to-end estimation procedure retains theoretical properties while learning flexible representations.
- This approach is competitive across common causal inference tasks.
- The study highlights the importance of design-based weights in various statistical applications.
Statistics > Machine Learning arXiv:2409.16407 (stat) [Submitted on 24 Sep 2024 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Towards Representation Learning for Weighting Problems in Design-Based Causal Inference Authors:Oscar Clivio, Avi Feller, Chris Holmes View a PDF of the paper titled Towards Representation Learning for Weighting Problems in Design-Based Causal Inference, by Oscar Clivio and 2 other authors View PDF HTML (experimental) Abstract:Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospective cohort studies, survey weighting, and the weighting portion of augmented weighting estimators. In such applications, we explore the central role of representation learning in finding desirable weights in practice. Unlike the common approach of assuming a well-specified representation, we highlight the error due to the choice of a representation and outline a general framework for finding suitable representations that minimize this error. Building on recent work that combines balancing weights and neural networks, we propose an end-to-end estimation procedure that learns a flexible representation, whi...
