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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.17043 |
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Table of Contents:
- We adapt Gaussian processes for estimating the average dose-response function in observational settings, introducing a powerful complement to treatment effect estimation for understanding heterogeneous effects. We incorporate samples from a Gaussian process posterior for the propensity score into a Gaussian process response model using Girard's approach to integrating over uncertainty in training data. We show Girard's method admits a positive-definite kernel, and provide theoretical justification by identifying it with an inner product of kernel mean embeddings. We demonstrate double robustness of our approach under a misspecified response function or propensity score. We characterize and mitigate regularization-induced confounding in Gaussian process response models. We show improvement over other methods for average dose-response function estimation in terms of coverage of the dose-response function and estimation bias, with less sensitivity to misspecification across experiments.