Treatment Effects in Extreme Regimes
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2023
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916256091734016 |
|---|---|
| author | Aloui, Ahmed Hasan, Ali Ng, Yuting Pajic, Miroslav Tarokh, Vahid |
| author_facet | Aloui, Ahmed Hasan, Ali Ng, Yuting Pajic, Miroslav Tarokh, Vahid |
| contents | Understanding treatment effects in extreme regimes is important for characterizing risks associated with different interventions. This is hindered by the unavailability of counterfactual outcomes and the rarity and difficulty of collecting extreme data in practice. To address this issue, we propose a new framework based on extreme value theory for estimating treatment effects in extreme regimes. We quantify these effects using variations in tail decay rates of potential outcomes in the presence and absence of treatments. We establish algorithms for calculating these quantities and develop related theoretical results. We demonstrate the efficacy of our approach on various standard synthetic and semi-synthetic datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_11697 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Treatment Effects in Extreme Regimes Aloui, Ahmed Hasan, Ali Ng, Yuting Pajic, Miroslav Tarokh, Vahid Methodology Machine Learning Understanding treatment effects in extreme regimes is important for characterizing risks associated with different interventions. This is hindered by the unavailability of counterfactual outcomes and the rarity and difficulty of collecting extreme data in practice. To address this issue, we propose a new framework based on extreme value theory for estimating treatment effects in extreme regimes. We quantify these effects using variations in tail decay rates of potential outcomes in the presence and absence of treatments. We establish algorithms for calculating these quantities and develop related theoretical results. We demonstrate the efficacy of our approach on various standard synthetic and semi-synthetic datasets. |
| title | Treatment Effects in Extreme Regimes |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2306.11697 |