Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866914047208718336 |
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| author | Lee, Byeonghee Kang, Joonsung |
| author_facet | Lee, Byeonghee Kang, Joonsung |
| contents | This paper develops a unified framework for estimating continuous outcomes under multiple treatment levels in observational studies. We integrate the Generalized Propensity Score (GPS), Covariate Balancing Propensity Score (CBPS), and outcome regression into a Penalized Empirical Likelihood (PEL) formulation. The GPS is parameterized by $\boldsymbolβ$ and denoted $π_{\boldsymbolβ}(\mathbf{X})$, while CBPS imposes moment conditions to ensure covariate balance. Outcome regression flexibly models the continuous response $Y$, and doubly robust estimation ensures consistency under either correct model specification. PEL allows simultaneous estimation and variable selection using general estimating equations. Simulation results and comparisons with state-of-the-art meta-learners confirm the effectiveness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood Lee, Byeonghee Kang, Joonsung Methodology This paper develops a unified framework for estimating continuous outcomes under multiple treatment levels in observational studies. We integrate the Generalized Propensity Score (GPS), Covariate Balancing Propensity Score (CBPS), and outcome regression into a Penalized Empirical Likelihood (PEL) formulation. The GPS is parameterized by $\boldsymbolβ$ and denoted $π_{\boldsymbolβ}(\mathbf{X})$, while CBPS imposes moment conditions to ensure covariate balance. Outcome regression flexibly models the continuous response $Y$, and doubly robust estimation ensures consistency under either correct model specification. PEL allows simultaneous estimation and variable selection using general estimating equations. Simulation results and comparisons with state-of-the-art meta-learners confirm the effectiveness of our method. |
| title | Doubly Robust Estimation of Continuous Outcomes under Multiple Treatment Levels via GPS, CBPS, and Penalized Empirical Likelihood |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.15846 |