Tisane：通过概念和数据关系的形式化推理创作统计模型
Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships
摘要：
正确的统计建模结合了关于概念如何关联的领域理论和如何测量数据的细节。然而，数据分析师目前缺乏工具支持，无法以集成的方式记录和推理领域假设、数据汇集和建模选择，从而导致可能危及科学有效性的错误。例如，广义线性混合效应模型有助于回答复杂的研究问题，但忽略随机效应会损害结果的普遍性。为了满足这一需求，我们提出了Tisane，一个混合主动系统，用于编写具有和不具有混合效应的广义线性模型。Tisane引入了一种研究设计规约语言，用于表达和询问有关变量之间关系的问题。Tisane贡献了一个交互式编译过程，该过程在图中表示关系，推断候选统计模型，并提出后续问题以消除用户查询的歧义，从而构建有效的模型。在对三名研究人员的案例研究中，我们发现Tisane帮助他们专注于他们的目标和假设，同时避免过去的错误。
英文摘要：
Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can compromise scientific validity. For instance, generalized linear mixed-effects models (GLMMs) help answer complex research questions, but omitting random effects impairs the generalizability of results. To address this need, we present Tisane, a mixed-initiative system for authoring generalized linear models with and without mixed-effects. Tisane introduces a study design specification language for expressing and asking questions about relationships between variables. Tisane contributes an interactive compilation process that represents relationships in a graph, infers candidate statistical models, and asks follow-up questions to disambiguate user queries to construct a valid model. In case studies with three researchers, we find that Tisane helps them focus on their goals and assumptions while avoiding past mistakes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
