Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2021
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| Acceso en línea: | |
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| _version_ | 1866912009149218816 |
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| author | Sakaguchi, Shosei |
| author_facet | Sakaguchi, Shosei |
| contents | Many policies involve dynamics in their treatment assignments, where individuals receive sequential interventions over multiple stages. We study estimation of an optimal dynamic treatment regime that guides the optimal treatment assignment for each individual at each stage based on their history. We propose an empirical welfare maximization approach in this dynamic framework, which estimates the optimal dynamic treatment regime using data from an experimental or quasi-experimental study while satisfying exogenous constraints on policies. The paper proposes two estimation methods: one solves the treatment assignment problem sequentially through backward induction, and the other solves the entire problem simultaneously across all stages. We establish finite-sample upper bounds on worst-case average welfare regrets for these methods and show their optimal $n^{-1/2}$ convergence rates. We also modify the simultaneous estimation method to accommodate intertemporal budget/capacity constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2106_05031 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints Sakaguchi, Shosei Econometrics Methodology Machine Learning Many policies involve dynamics in their treatment assignments, where individuals receive sequential interventions over multiple stages. We study estimation of an optimal dynamic treatment regime that guides the optimal treatment assignment for each individual at each stage based on their history. We propose an empirical welfare maximization approach in this dynamic framework, which estimates the optimal dynamic treatment regime using data from an experimental or quasi-experimental study while satisfying exogenous constraints on policies. The paper proposes two estimation methods: one solves the treatment assignment problem sequentially through backward induction, and the other solves the entire problem simultaneously across all stages. We establish finite-sample upper bounds on worst-case average welfare regrets for these methods and show their optimal $n^{-1/2}$ convergence rates. We also modify the simultaneous estimation method to accommodate intertemporal budget/capacity constraints. |
| title | Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints |
| topic | Econometrics Methodology Machine Learning |
| url | https://arxiv.org/abs/2106.05031 |