VAINE: Visualization and AI for Natural Experiments
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2021
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| Materias: | |
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| _version_ | 1866916224569442304 |
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| author | Guo, Grace Glenski, Maria Shaw, ZhuanYi Saldanha, Emily Endert, Alex Volkova, Svitlana Arendt, Dustin |
| author_facet | Guo, Grace Glenski, Maria Shaw, ZhuanYi Saldanha, Emily Endert, Alex Volkova, Svitlana Arendt, Dustin |
| contents | Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson's paradox through two usage scenarios. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2109_04348 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | VAINE: Visualization and AI for Natural Experiments Guo, Grace Glenski, Maria Shaw, ZhuanYi Saldanha, Emily Endert, Alex Volkova, Svitlana Arendt, Dustin Human-Computer Interaction Natural experiments are observational studies where the assignment of treatment conditions to different populations occurs by chance "in the wild". Researchers from fields such as economics, healthcare, and the social sciences leverage natural experiments to conduct hypothesis testing and causal effect estimation for treatment and outcome variables that would otherwise be costly, infeasible, or unethical. In this paper, we introduce VAINE (Visualization and AI for Natural Experiments), a visual analytics tool for identifying and understanding natural experiments from observational data. We then demonstrate how VAINE can be used to validate causal relationships, estimate average treatment effects, and identify statistical phenomena such as Simpson's paradox through two usage scenarios. |
| title | VAINE: Visualization and AI for Natural Experiments |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2109.04348 |