Doubly Robust Inference in Causal Latent Factor Models
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866914996262273024 |
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| author | Abadie, Alberto Agarwal, Anish Dwivedi, Raaz Shah, Abhin |
| author_facet | Abadie, Alberto Agarwal, Anish Dwivedi, Raaz Shah, Abhin |
| contents | This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, inverse probability weighting, and a novel cross-fitting procedure for matrix completion. We derive finite-sample and asymptotic guarantees, and show that the error of the new estimator converges to a mean-zero Gaussian distribution at a parametric rate. Simulation results demonstrate the relevance of the formal properties of the estimators analyzed in this article. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Doubly Robust Inference in Causal Latent Factor Models Abadie, Alberto Agarwal, Anish Dwivedi, Raaz Shah, Abhin Econometrics Machine Learning Methodology This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, inverse probability weighting, and a novel cross-fitting procedure for matrix completion. We derive finite-sample and asymptotic guarantees, and show that the error of the new estimator converges to a mean-zero Gaussian distribution at a parametric rate. Simulation results demonstrate the relevance of the formal properties of the estimators analyzed in this article. |
| title | Doubly Robust Inference in Causal Latent Factor Models |
| topic | Econometrics Machine Learning Methodology |
| url | https://arxiv.org/abs/2402.11652 |