Co-Developing Causal Graphs with Domain Experts Guided by Weighted FDR-Adjusted p-values
以加权多重检验调整后的 p 值辅助领域专家共建因果图AI 解读 ↓
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以加权多重检验调整后的 p 值辅助领域专家共建因果图
- 研究问题
- 如何让领域专家与统计建模者共同构建因果图，并将专家信念和经多重检验调整的统计证据结合到反复修订图结构的过程？
- 方法与思路
- 作者从专家初始因果图出发，对相关强度、效应方向和模型协方差拟合设置检验，并将专家排序纳入 FDCR 调整后反馈图结构修订。
- 材料陈述的贡献
- 摘要提出一种专家知识与数据反馈结合的迭代共建流程，通过在图上呈现加权调整后的 p 值，辅助讨论哪些连接获得支持或受到质疑。
- 阅读关注点
- 应核对检验所需的因果识别条件、专家权重的含义、FDCR 与 FDR 的区别，以及反复修改图结构后错误率控制是否仍然成立。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；统计关联及调整后的 p 值不能单独确定因果方向，摘要未给出完整识别和迭代保证。
原文摘要
This paper proposes an approach facilitating co-design of causal graphs between subject matter experts and statistical modellers. Modern causal analysis starting with formulation of causal graphs provides benefits for robust analysis and well-grounded decision support. Moreover, this process can enrich the discovery and planning phase of data science projects. The key premise is that plotting relevant statistical information on a causal graph structure can facilitate an intuitive discussion between domain experts and modellers. Furthermore, Hand-crafting causality graphs, integrating human expertise with robust statistical methodology, enables ensuring responsible AI practices. The paper focuses on using multiplicity-adjusted p-values, controlling for the false discovery rate (FDR), as an aid for co-designing the graph. A family of hypotheses relevant to causal graph construction is identified, including assessing correlation strengths, directions of causal effects, and how well an estimated structural causal model induces the observed covariance structure. An iterative flow is described where an initial causal graph is drafted based on expert beliefs about likely causal relationships. The subject matter expert's beliefs, communicated as ranked scores could be incorporated into the control of the measure proposed by Benjamini and Kling, the FDCR (False Discovery Cost Rate). The FDCR-adjusted p-values then provide feedback on which parts of the graph are supported or contradicted by the data. This co-design process continues, adding, removing, or revising arcs in the graph, until the expert and modeller converge on a satisfactory causal structure grounded in both domain knowledge and data evidence.