Causal Responder Detection
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916299923259392 |
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| author | Frostig, Tzviel Machluf, Oshri Kamber, Amitay Berkman, Elad Pryluk, Raviv |
| author_facet | Frostig, Tzviel Machluf, Oshri Kamber, Amitay Berkman, Elad Pryluk, Raviv |
| contents | We introduce the causal responders detection (CARD), a novel method for responder analysis that identifies treated subjects who significantly respond to a treatment. Leveraging recent advances in conformal prediction, CARD employs machine learning techniques to accurately identify responders while controlling the false discovery rate in finite sample sizes. Additionally, we incorporate a propensity score adjustment to mitigate bias arising from non-random treatment allocation, enhancing the robustness of our method in observational settings. Simulation studies demonstrate that CARD effectively detects responders with high power in diverse scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17571 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Causal Responder Detection Frostig, Tzviel Machluf, Oshri Kamber, Amitay Berkman, Elad Pryluk, Raviv Methodology Applications Machine Learning We introduce the causal responders detection (CARD), a novel method for responder analysis that identifies treated subjects who significantly respond to a treatment. Leveraging recent advances in conformal prediction, CARD employs machine learning techniques to accurately identify responders while controlling the false discovery rate in finite sample sizes. Additionally, we incorporate a propensity score adjustment to mitigate bias arising from non-random treatment allocation, enhancing the robustness of our method in observational settings. Simulation studies demonstrate that CARD effectively detects responders with high power in diverse scenarios. |
| title | Causal Responder Detection |
| topic | Methodology Applications Machine Learning |
| url | https://arxiv.org/abs/2406.17571 |